Data engineer jobs in Santa Rosa, CA

Staff Data Scientist | San Francisco | $250K-$300K + Equity We're partnering with one of the fastest-growing AI companies in the world to hire a Staff Data Scientist. Backed by over $230M from top-tier investors and already valued at over $1B, they've secured customers that include some of the most recognizable names in tech. Their AI platform powers millions of daily interactions and is quickly becoming the enterprise standard for conversational AI. In this role, you'll bring rigorous analytics and experimentation leadership that directly shapes product strategy and company performance. What you'll do: Drive deep-dive analyses on user behavior, product performance, and growth drivers Design and interpret A/B tests to measure product impact at scale Build scalable data models, pipelines, and dashboards for company-wide use Partner with Product and Engineering to embed experimentation best practices Evaluate ML models, ensuring business relevance, performance, and trade-off clarity What we're looking for: 5+ years in data science or product analytics at scale (consumer or marketplace preferred) Advanced SQL and Python skills, with strong foundations in statistics and experimental design Proven record of designing, running, and analyzing large-scale experiments Ability to analyze and reason about ML models (classification, recommendation, LLMs) Strong communicator with a track record of influencing cross-functional teams If you're excited by the sound of this challenge- apply today and we'll be in touch.

We're working with a Series A health tech start-up pioneering a revolutionary approach to healthcare AI, developing neurosymbolic systems that combine statistical learning with structured medical knowledge. Their technology is being adopted by leading health systems and insurers to enhance patient outcomes through advanced predictive analytics. We're seeking Machine Learning Engineers who excel at the intersection of data science, modeling, and software engineering. You'll design and implement models that extract insights from longitudinal healthcare data, balancing analytical rigor, interpretability, and scalability. This role offers a unique opportunity to tackle foundational modeling challenges in healthcare, where your contributions will directly influence clinical, actuarial, and policy decisions. Key Responsibilities Develop predictive models to forecast disease progression, healthcare utilization, and costs using temporal clinical data (claims, EHR, laboratory results, pharmacy records) Design interpretable and explainable ML solutions that earn the trust of clinicians, actuaries, and healthcare decision-makers Research and prototype innovative approaches leveraging both classical and modern machine learning techniques Build robust, scalable ML pipelines for training, validation, and deployment in distributed computing environments Collaborate cross-functionally with data engineers, clinicians, and product teams to ensure models address real-world healthcare needs Communicate findings and methodologies effectively through visualizations, documentation, and technical presentations Required Qualifications Strong foundation in statistical modeling, machine learning, or data science, with preference for experience in temporal or longitudinal data analysis Proficiency in Python and ML frameworks (PyTorch, JAX, NumPyro, PyMC, etc.) Proven track record of transitioning models from research prototypes to production systems Experience with probabilistic methods, survival analysis, or Bayesian inference (highly valued) Bonus Qualifications Experience working with clinical data and healthcare terminologies (ICD, CPT, SNOMED CT, LOINC) Background in actuarial modeling, claims forecasting, or risk adjustment methodologies

🌎 San Francisco (Hybrid) 💼 Founding/Staff Data Engineer 💵 $200-300k base Our client is an elite applied AI research and product lab building AI-native systems for finance-and pushing frontier models into real production environments. Their work sits at the intersection of data, research, and high-stakes financial decision-making. As the Founding Data Engineer, you will own the data platform that powers everything: models, experiments, and user-facing products relied on by demanding financial customers. You'll make foundational architectural decisions, work directly with researchers and product engineers, and help define how data is built, trusted, and scaled from day one. What you'll do: Design and build the core data platform, ingesting, transforming, and serving large-scale financial and alternative datasets. Partner closely with researchers and ML engineers to ship production-grade data and feature pipelines that power cutting-edge models. Establish data quality, observability, lineage, and reproducibility across both experimentation and production workloads. Deploy and operate data services using Docker and Kubernetes in a modern cloud environment (AWS, GCP, or Azure). Make foundational choices on tooling, architecture, and best practices that will define how data works across the company. Continuously simplify and evolve systems-rewriting pipelines or infrastructure when it's the right long-term decision. Ideal candidate: Have owned or built high-performance data systems end-to-end, directly supporting production applications and ML models. Are strongest in backend and data infrastructure, with enough frontend literacy to integrate cleanly with web products when needed. Can design and evolve backend services and pipelines (Node.js or Python) to support new product features and research workflows. Are an expert in at least one statically typed language, with a strong bias toward type safety, correctness, and maintainable systems. Have deployed data workloads and services using Docker and Kubernetes on a major cloud provider. Are comfortable making hard calls-simplifying, refactoring, or rebuilding legacy pipelines when quality and scalability demand it. Use AI tools to accelerate your work, but rigorously review and validate AI-generated code, insisting on sound system design. Thrive in a high-bar, high-ownership environment with other exceptional engineers. Love deep technical problems in data infrastructure, distributed systems, and performance. Nice to have: Experience working with financial data (market, risk, portfolio, transactional, or alternative datasets). Familiarity with ML infrastructure, such as feature stores, experiment tracking, or model serving systems. Background in a high-growth startup or a foundational infrastructure role. Compensation & setup: Competitive salary and founder-level equity Hybrid role based in San Francisco, with close collaboration and significant ownership Small, elite team building core infrastructure with outsized impact

Member of Technical Staff - AI Data Engineer San Francisco (In-Office) $150K to $225K + Equity A high-growth, AI-native startup coming out of stealth is hiring AI Data Engineers to build the systems that power production-grade AI. The company has recently signed a Series A term sheet and is scaling rapidly. This role is central to unblocking current bottlenecks across data engineering, context modeling, and agent performance. Responsibilities: • Build distributed, reliable data pipelines using Airflow, Temporal, and n8n • Model SQL, vector, and NoSQL databases (Postgres, Qdrant, etc.) • Build API and function-based services in Python • Develop custom automations (Playwright, Stagehand, Zapier) • Work with AI researchers to define and expose context as services • Identify gaps in data quality and drive changes to upstream processes • Ship fast, iterate, and own outcomes end-to-end Required Experience: • Strong background in data engineering • Hands-on experience working with LLMs or LLM-powered applications • Data modeling skills across SQL and vector databases • Experience building distributed systems • Experience with Airflow, Temporal, n8n, or similar workflow engines • Python experience (API/services) • Startup mindset and bias toward rapid execution Nice To Have: • Experience with stream processing (Flink) • dbt or Clickhouse experience • CDC pipelines • Experience with context construction, RAG, or agent workflows • Analytical tooling (Posthog) What You Can Expect: • High-intensity, in-office environment • Fast decision-making and rapid shipping cycles • Real ownership over architecture and outcomes • Opportunity to work on AI systems operating at meaningful scale • Competitive compensation package • Meals provided plus full medical, dental, and vision benefits If this sounds like you, please apply now.

The Company: A data services company based in the heart of San Francisco, are looking for a Senior Data Engineer. They are a team of passionate engineers and data experts that are working on a variety of different project, primarily in the financial services sector, helping organizations build scalable, modern data platforms. This is a hands-on, full-time role with close collaboration alongside the CTO and senior engineers, offering strong influence over technical direction and delivery. The Role: This is an on-site position in the downtown San Francisco where you will be working as part of a close-knit team, collaborating on projects in their brand new office. You will be working across end-to-end data projects, including: Building and maintaining data pipelines and ETL processes. Sourcing and integrating third-party APIs and datasets. Batch and near-real-time processing (cloud agnostic). Downstream analytics and reporting using tools like Sigma Computing and Omnium Analytics. Collaborating with the CTO and engineering team to deliver client solutions. Key Skills: 5+ years' data engineering experience Strong Python, BigQuery, and cloud (GCP or similar) Solid ETL and pipeline background Comfortable with large-scale data Nice to Have Beam, Dataflow, Spark, or Hadoop Tableau or Looker ML/AI exposure Kafka or Pub/Sub Given the varied nature of the work, a broad range of technology experience is valued. You don't need to have experience with every tool listed below to be considered, so we encourage you to apply. This role is 5 days a week on-site in downtown San Francisco. Looking to pay between $170,000-$220,000 with a bonus between 15-20%. Benefits Health, Dental & Vision covered Unlimited PTO 401(k) with employer contribution Commuter benefits.

If you're hands on with modern data platforms, cloud tech, and big data tools and you like building solutions that are secure, repeatable, and fast, this role is for you. As a Senior Data Engineer, you will design, build, and maintain scalable data pipelines that transform raw information into actionable insights. The ideal candidate will have strong experience across modern data platforms, cloud environments, and big data technologies, with a focus on building secure, repeatable, and high-performing solutions. Responsibilities: Design, develop, and maintain secure, scalable data pipelines to ingest, transform, and deliver curated data into the Common Data Platform (CDP). Participate in Agile rituals and contribute to delivery within the Scaled Agile Framework (SAFe). Ensure quality and reliability of data products through automation, monitoring, and proactive issue resolution. Deploy alerting and auto-remediation for pipelines and data stores to maximize system availability. Apply a security first and automation-driven approach to all data engineering practices. Collaborate with cross-functional teams (data scientists, analysts, product managers, and business stakeholders) to align infrastructure with evolving data needs. Stay current on industry trends and emerging tools, recommending improvements to strengthen efficiency and scalability. Qualifications: Bachelor's degree in Computer Science, Information Systems, or related field (or equivalent experience). At least 3 years of experience with Python and PySpark, including Jupyter notebooks and unit testing. At least 2 years of experience with Databricks, Collibra, and Starburst. Proven work with relational and NoSQL databases, including STAR and dimensional modeling approaches. Hands-on experience with modern data stacks: object stores (S3), Spark, Airflow, lakehouse architectures, and cloud warehouses (Snowflake, Redshift). Strong background in ETL and big data engineering (on-prem and cloud). Work within enterprise cloud platforms (CFS2, Cloud Foundational Services 2/EDS) for governance and compliance. Experience building end-to-end pipelines for structured, semi-structured, and unstructured data using Spark.

Job Title: Data Engineer - Retail / E-Commerce 🏢 Company: Aaratech Inc 🛑 Eligibility: Only U.S. Citizens and Green Card holders are eligible. Please note that we do not offer visa sponsorship. Aaratech Inc. is seeking a results-driven Data Engineer - Retail / E-Commerce to support customer, sales, and product data platforms. The role focuses on building scalable pipelines that enable real-time and batch analytics for business growth. Key Responsibilities: 🔹 Data Pipeline Development Develop and maintain data pipelines for sales, customer, and product data. Integrate data from e-commerce platforms and marketing systems. 🔹 Data Modeling Design data models to support analytics and BI reporting. Optimize performance and scalability. 🔹 Data Quality Ensure data accuracy, completeness, and consistency. Implement monitoring and error-handling processes. 🔹 Collaboration Work closely with data analysts, product, and marketing teams. 🔹 Tools & Technologies Use SQL, Python, ETL tools, and cloud data platforms. Qualifications: ✅ Bachelor's degree in Computer Science, Engineering, or related field ✅ Minimum 2 years of Data Engineering experience ✅ Strong SQL and Python skills ✅ Experience with data pipelines and analytics platforms ✅ Retail or e-commerce data experience preferred ✅ Strong problem-solving skills

We imagine a world where new medicines reach patients in months, not years, and where scientific breakthroughs happen at the speed of thought. Mithrl is building the world's first commercially available AI Co-Scientist. It is a discovery engine that transforms messy biological data into insights in minutes. Scientists ask questions in natural language, and Mithrl responds with analysis, novel targets, hypotheses, and patent-ready reports. No coding. No waiting. No bioinformatics bottlenecks. We are one of the fastest growing tech bio companies in the Bay Area with 12x year over year revenue growth. Our platform is used across three continents by leading biotechs and big pharmas. We power breakthroughs from early target discovery to mechanism-of-action. And we are just getting started. ABOUT THE ROLE We are hiring a Data Engineer, Knowledge Graphs to build the infrastructure that powers Mithrl's biological knowledge layer. You will partner closely with the Data Scientist, Knowledge Graphs to take curated knowledge sources and transform them into scalable, reliable, production ready systems that serve the entire platform. Your work includes building ETL pipelines for large biological datasets, designing schemas and storage models for graph structured data, and creating the API surfaces that allow ML engineers, application teams, and the AI Co-Scientist to query and use the knowledge graph efficiently. You will also own the reliability, performance, and versioning of knowledge graph infrastructure across releases. This role is the bridge between biological knowledge ingestion and the high performance engineering systems that use it. If you enjoy working on data modeling, schema design, graph storage, ETL, and scalable infrastructure, this is an opportunity to have deep impact on the intelligence layer of Mithrl. WHAT YOU WILL DO Build and maintain ETL pipelines for large public biological datasets and curated knowledge sources Design, implement, and evolve schemas and storage models for graph structured biological data Create efficient APIs and query surfaces that allow internal teams and AI systems to retrieve nodes, relationships, pathways, annotations, and graph analytics Partner closely with the Data Scientists to operationalize curated relationships, harmonized variable IDs, metadata standards, and ontology mappings Build data models that support multi tenant access, versioning, and reproducibility across releases Implement scalable storage and indexing strategies for high volume graph data Maintain data quality, validate data integrity, and build monitoring around ingestion and usage Work with ML engineers and application teams to ensure the knowledge graph infrastructure supports downstream reasoning, analysis, and discovery applications Support data warehousing, documentation, and API reliability Ensure performance, reliability, and uptime for knowledge graph services WHAT YOU BRING Required Qualifications Strong experience as a data engineer or backend engineer working with data intensive systems Experience building ETL or ELT pipelines for large structured or semi structured datasets Strong understanding of database design, schema modeling, and data architecture Experience with graph data models or willingness to learn graph storage concepts Proficiency in Python or similar languages for data engineering Experience designing and maintaining APIs for data access Understanding of versioning, provenance, validation, and reproducibility in data systems Experience with cloud infrastructure and modern data stack tools Strong communication skills and ability to work closely with scientific and engineering teams Nice to Have Experience with graph databases or graph query languages Experience with biological or chemical data sources Familiarity with ontologies, controlled vocabularies, and metadata standards Experience with data warehousing and analytical storage formats Previous work in a tech bio company or scientific platform environment WHAT YOU WILL LOVE AT MITHRL You will build the core infrastructure that makes the biological knowledge graph fast, reliable, and usable Team: Join a tight-knit, talent-dense team of engineers, scientists, and builders Culture: We value consistency, clarity, and hard work. We solve hard problems through focused daily execution Speed: We ship fast (2x/week) and improve continuously based on real user feedback Location: Beautiful SF office with a high-energy, in-person culture Benefits: Comprehensive PPO health coverage through Anthem (medical, dental, and vision) + 401(k) with top-tier plans

Midjourney is a research lab exploring new mediums to expand the imaginative powers of the human species. We are a small, self-funded team focused on design, human infrastructure, and AI. We have no investors, no big company controlling us, and no advertisers. We are 100% supported by our amazing community. Our tools are already used by millions of people to dream, to explore, and to create. But this is just the start. We think the story of the 2020s is about building the tools that will remake the world for the next century. We're making those tools, to expand what it means to be human. Core Responsibilities: Design and maintain data pipelines to consolidate information across multiple sources (subscription platforms, payment systems, infrastructure and usage monitoring, and financial systems) into a unified analytics environment Build and manage interactive dashboards and self-service BI tools that enable leadership to track key business metrics including revenue performance, infrastructure costs, customer retention, and operational efficiency Serve as technical owner of our financial planning platform (Pigment or similar), leading implementation and build-out of models, data connections, and workflows in partnership with Finance leadership to translate business requirements into functional system architecture Develop automated data quality checks and cleaning processes to ensure accuracy and consistency across financial and operational datasets Partner with Finance, Product and Operations teams to translate business questions into analytical frameworks, including cohort analysis, cost modeling, and performance trending Create and maintain documentation for data models, ETL processes, dashboard logic, and system workflows to ensure knowledge continuity Support strategic planning initiatives by building financial models, scenario analyses, and data-driven recommendations for resource allocation and growth investments Required Qualifications: 3-5+ years experience in data engineering, analytics engineering, or similar role with demonstrated ability to work with large-scale datasets Strong SQL skills and experience with modern data warehousing solutions (BigQuery, Snowflake, Redshift, etc.) Proficiency in at least one programming language (Python, R) for data manipulation and analysis Experience with BI/visualization tools (Looker, Tableau, Power BI, or similar) Hands-on experience administering enterprise financial systems (NetSuite, SAP, Oracle, or similar ERP platforms) Experience working with Stripe Billing or similar subscription management platforms, including data extraction and revenue reporting Ability to communicate technical concepts clearly to non-technical stakeholders

Citizenship Requirement: U.S. Citizens Only ITTConnect is seeking a Data Engineer / Analytics to work for one of our clients, a major Technology Consulting firm with headquarters in Europe. They are experts in tailored technology consulting and services to banks, investment firms and other Financial vertical clients. Job location: San Francisco Bay area or NY City. Work Model: Ability to come into the office as requested Seniority: 10+ years of total experience About the role: The Data Engineer / Analytics Specialist will support analytics, product insights, and AI initiatives. You will build robust data pipelines, integrate data sources, and enhance the organization's analytical foundations. Responsibilities: Build and operate Snowflake-based analytics environments. Develop ETL/ELT pipelines (DBT, Airflow, etc.). Integrate APIs, external data sources, and streaming inputs. Perform query optimization, basic data modeling, and analytics support. Enable downstream GenAI and analytics use cases. Requirements: 10+ years of overall technology experience 3+ years hands-on AWS experience required Strong SQL and Snowflake experience. Hands-on pipeline engineering with DBT, Airflow, or similar. Experience with API integrations and modern data architectures.

About us: Intuitive is an innovation-led engineering company delivering business outcomes for 100's of Enterprises globally. With the reputation of being a Tiger Team & a Trusted Partner of enterprise technology leaders, we help solve the most complex Digital Transformation challenges across following Intuitive Superpowers: Modernization & Migration Application & Database Modernization Platform Engineering (IaC/EaC, DevSecOps & SRE) Cloud Native Engineering, Migration to Cloud, VMware Exit FinOps Data & AI/ML Data (Cloud Native / DataBricks / Snowflake) Machine Learning, AI/GenAI Cybersecurity Infrastructure Security Application Security Data Security AI/Model Security SDx & Digital Workspace (M365, G-suite) SDDC, SD-WAN, SDN, NetSec, Wireless/Mobility Email, Collaboration, Directory Services, Shared Files Services Intuitive Services: Professional and Advisory Services Elastic Engineering Services Managed Services Talent Acquisition & Platform Resell Services About the job: Title: Imaging Data Engineer/Architect Start Date: Immediate # of Positions: 1 Position Type: Contract/ Full-Time Location: San Francisco, CA Notes: Imaging data Engineer/architect who understands Radiology and Digital pathology, related clinical data and metadata. Hands-on experience on above technologies, and with good knowledge in the biomedical imaging, and data pipelines overall. About the Role We are seeking a highly skilled Imaging Data Engineer/Architect to join our San Francisco team as a Subject Matter Expert (SME) in radiology and digital pathology. This role will design and manage imaging data pipelines, ensuring seamless integration of clinical data and metadata to support advanced diagnostic and research applications. The ideal candidate will have deep expertise in medical imaging standards, cloud-based data architectures, and healthcare interoperability, contributing to innovative solutions that enhance patient outcomes. Responsibilities Design and implement scalable data architectures for radiology and digital pathology imaging data, including DICOM, HL7, and FHIR standards. Develop and optimize data pipelines to process and store large-scale imaging datasets (e.g., MRI, CT, histopathology slides) and associated metadata. Collaborate with clinical teams to understand radiology and pathology workflows, ensuring data solutions align with clinical needs. Ensure data integrity, security, and compliance with healthcare regulations (e.g., HIPAA, GDPR). Integrate imaging data with AI/ML models for diagnostic and predictive analytics, working closely with data scientists. Build and maintain metadata schemas to support data discoverability and interoperability across systems. Provide technical expertise to cross-functional teams, including product managers and software engineers, to drive imaging data strategy. Conduct performance tuning and optimization of imaging data storage and retrieval systems in cloud environments (e.g., AWS, Google Cloud, Azure). Document data architectures and processes, ensuring knowledge transfer to internal teams and external partners. Stay updated on emerging imaging technologies and standards, proposing innovative solutions to enhance data workflows. Qualifications Education: Bachelor's degree in computer science, Biomedical Engineering, or a related field (master's preferred). Experience: 5+ years in data engineering or architecture, with at least 3 years focused on medical imaging (radiology and/or digital pathology). Proven experience with DICOM, HL7, FHIR, and imaging metadata standards (e.g., SNOMED, LOINC). Hands-on experience with cloud platforms (AWS, Google Cloud, or Azure) for imaging data storage and processing. Technical Skills: Proficiency in programming languages (e.g., Python, Java, SQL) for data pipeline development. Expertise in ETL processes, data warehousing, and database management (e.g., Snowflake, BigQuery, PostgreSQL). Familiarity with AI/ML integration for imaging data analytics. Knowledge of containerization (e.g., Docker, Kubernetes) for deploying data solutions. Domain Knowledge: Deep understanding of radiology and digital pathology workflows, including PACS and LIS systems. Familiarity with clinical data integration and healthcare interoperability standards. Soft Skills: Strong analytical and problem-solving skills to address complex data challenges. Excellent communication skills to collaborate with clinical and technical stakeholders. Ability to work independently in a fast-paced environment, with a proactive approach to innovation. Certifications (preferred): AWS Certified Solutions Architect, Google Cloud Professional Data Engineer, or equivalent. Certifications in medical imaging (e.g., CIIP - Certified Imaging Informatics Professional).

Salary: $200-250k base + RSUs This fast-growing Series E AI SaaS company is redefining how modern engineering teams build and deploy applications. We're looking for a Staff Data Scientist to drive Sales and Go-to-Market (GTM) analytics, applying advanced modeling and experimentation to accelerate revenue growth and optimize the full sales funnel. About the Role As the senior data scientist supporting Sales and GTM, you will combine statistical modeling, experimentation, and advanced analytics to inform strategy and guide decision-making across our revenue organization. Your work will help leadership understand pipeline health, predict outcomes, and identify the levers that unlock sustainable growth. Key Responsibilities Model the Business: Build forecasting and propensity models for pipeline generation, conversion rates, and revenue projections. Optimize the Sales Funnel: Analyze lead scoring, opportunity progression, and deal velocity to recommend improvements in acquisition, qualification, and close rates. Experimentation & Causal Analysis: Design and evaluate experiments (A/B tests, uplift modeling) to measure the impact of pricing, incentives, and campaign initiatives. Advanced Analytics for GTM: Apply machine learning and statistical techniques to segment accounts, predict churn/expansion, and identify high-value prospects. Cross-Functional Partnership: Work closely with Sales, Marketing, RevOps, and Product to influence GTM strategy and ensure data-driven decisions. Data Infrastructure Collaboration: Partner with Analytics Engineering to define data requirements, ensure data quality, and enable self-serve reporting. Strategic Insights: Present findings to executive leadership, translating complex analyses into actionable recommendations. About You Experience: 6+ years in data science or advanced analytics roles, with significant time spent in B2B SaaS or developer tools environments. Technical Depth: Expert in SQL and proficient in Python or R for statistical modeling, forecasting, and machine learning. Domain Knowledge: Strong understanding of sales analytics, revenue operations, and product-led growth (PLG) motions. Analytical Rigor: Skilled in experimentation design, causal inference, and building predictive models that influence GTM strategy. Communication: Exceptional ability to tell a clear story with data and influence senior stakeholders across technical and business teams. Business Impact: Proven record of driving measurable improvements in pipeline efficiency, conversion rates, or revenue outcomes.

Staff Data Scientist | San Francisco | $250K-$300K + Equity We're partnering with one of the fastest-growing AI companies in the world to hire a Staff Data Scientist. Backed by over $230M from top-tier investors and already valued at over $1B, they've secured customers that include some of the most recognizable names in tech. Their AI platform powers millions of daily interactions and is quickly becoming the enterprise standard for conversational AI. In this role, you'll bring rigorous analytics and experimentation leadership that directly shapes product strategy and company performance. What you'll do: Drive deep-dive analyses on user behavior, product performance, and growth drivers Design and interpret A/B tests to measure product impact at scale Build scalable data models, pipelines, and dashboards for company-wide use Partner with Product and Engineering to embed experimentation best practices Evaluate ML models, ensuring business relevance, performance, and trade-off clarity What we're looking for: 5+ years in data science or product analytics at scale (consumer or marketplace preferred) Advanced SQL and Python skills, with strong foundations in statistics and experimental design Proven record of designing, running, and analyzing large-scale experiments Ability to analyze and reason about ML models (classification, recommendation, LLMs) Strong communicator with a track record of influencing cross-functional teams If you're excited by the sound of this challenge- apply today and we'll be in touch.

🌎 San Francisco (Hybrid) 💼 Founding/Staff Data Engineer 💵 $200-300k base Our client is an elite applied AI research and product lab building AI-native systems for finance-and pushing frontier models into real production environments. Their work sits at the intersection of data, research, and high-stakes financial decision-making. As the Founding Data Engineer, you will own the data platform that powers everything: models, experiments, and user-facing products relied on by demanding financial customers. You'll make foundational architectural decisions, work directly with researchers and product engineers, and help define how data is built, trusted, and scaled from day one. What you'll do: Design and build the core data platform, ingesting, transforming, and serving large-scale financial and alternative datasets. Partner closely with researchers and ML engineers to ship production-grade data and feature pipelines that power cutting-edge models. Establish data quality, observability, lineage, and reproducibility across both experimentation and production workloads. Deploy and operate data services using Docker and Kubernetes in a modern cloud environment (AWS, GCP, or Azure). Make foundational choices on tooling, architecture, and best practices that will define how data works across the company. Continuously simplify and evolve systems-rewriting pipelines or infrastructure when it's the right long-term decision. Ideal candidate: Have owned or built high-performance data systems end-to-end, directly supporting production applications and ML models. Are strongest in backend and data infrastructure, with enough frontend literacy to integrate cleanly with web products when needed. Can design and evolve backend services and pipelines (Node.js or Python) to support new product features and research workflows. Are an expert in at least one statically typed language, with a strong bias toward type safety, correctness, and maintainable systems. Have deployed data workloads and services using Docker and Kubernetes on a major cloud provider. Are comfortable making hard calls-simplifying, refactoring, or rebuilding legacy pipelines when quality and scalability demand it. Use AI tools to accelerate your work, but rigorously review and validate AI-generated code, insisting on sound system design. Thrive in a high-bar, high-ownership environment with other exceptional engineers. Love deep technical problems in data infrastructure, distributed systems, and performance. Nice to have: Experience working with financial data (market, risk, portfolio, transactional, or alternative datasets). Familiarity with ML infrastructure, such as feature stores, experiment tracking, or model serving systems. Background in a high-growth startup or a foundational infrastructure role. Compensation & setup: Competitive salary and founder-level equity Hybrid role based in San Francisco, with close collaboration and significant ownership Small, elite team building core infrastructure with outsized impact

The Company: A data services company based in the heart of San Francisco, are looking for a Senior Data Engineer. They are a team of passionate engineers and data experts that are working on a variety of different project, primarily in the financial services sector, helping organizations build scalable, modern data platforms. This is a hands-on, full-time role with close collaboration alongside the CTO and senior engineers, offering strong influence over technical direction and delivery. The Role: This is an on-site position in the downtown San Francisco where you will be working as part of a close-knit team, collaborating on projects in their brand new office. You will be working across end-to-end data projects, including: Building and maintaining data pipelines and ETL processes. Sourcing and integrating third-party APIs and datasets. Batch and near-real-time processing (cloud agnostic). Downstream analytics and reporting using tools like Sigma Computing and Omnium Analytics. Collaborating with the CTO and engineering team to deliver client solutions. Key Skills: 5+ years' data engineering experience Strong Python, BigQuery, and cloud (GCP or similar) Solid ETL and pipeline background Comfortable with large-scale data Nice to Have Beam, Dataflow, Spark, or Hadoop Tableau or Looker ML/AI exposure Kafka or Pub/Sub Given the varied nature of the work, a broad range of technology experience is valued. You don't need to have experience with every tool listed below to be considered, so we encourage you to apply. This role is 5 days a week on-site in downtown San Francisco. Looking to pay between $170,000-$220,000 with a bonus between 15-20%. Benefits Health, Dental & Vision covered Unlimited PTO 401(k) with employer contribution Commuter benefits.

We're hiring a Senior/Lead Data Engineer to join a fast-growing AI startup. The team comes from a billion dollar AI company, and has raised a $40M+ seed round. You'll need to be comfortable transforming and moving data in a new 'group level' data warehouse, from legacy sources. You'll have a strong data modeling background. Proven proficiency in modern data transformation tools, specifically dbt and/or SQLMesh. Exceptional ability to apply systems thinking and complex problem-solving to ambiguous challenges. Experience within a high-growth startup environment is highly valued. Deep, practical knowledge of the entire data lifecycle, from generation and governance through to advanced downstream applications (e.g., fueling AI/ML models, LLM consumption, and core product features). Outstanding ability to communicate technical complexity clearly, synthesizing information into actionable frameworks for executive and cross-functional teams.

We're the data team behind Midjourney's image generation models. We handle the dataset side: processing, filtering, scoring, captioning, and all the distributed compute that makes high-quality training data possible. What you'd be working on: Large-scale dataset processing and filtering pipelines Training classifiers for content moderation and quality assessment Models for data quality and aesthetic evaluation Data visualization tools for experimenting on dataset samples Testing/simulating distributed inference pipelines Monitoring dashboards for data quality and pipeline health Performance optimization and infrastructure scaling Occasionally jumping into inference optimization and other cross-team projects Our current stack: PySpark, Slurm, distributed batch processing across hybrid cloud setup. We're pragmatic about tools - if there's something better, we'll switch. We're looking for someone strong in either: Data engineering/ML pipelines at scale, or Cloud/infrastructure with distributed systems experience Don't need exact tech matches - comfort with adjacent technologies and willingness to learn matters more. We work with our own hardware plus GCP and other providers, so adaptability across different environments is valuable. Location: SF office a few times per week (we may make exceptions on location for truly exceptional candidates) The role offers variety, our team members often get pulled into different projects across the company, from dataset work to inference optimization. If you're interested in the intersection of large-scale data processing and cutting-edge generative AI, we'd love to hear from you.

We are seeking an experienced Data Engineer to design and optimize scalable data pipelines that drive our global data and analytics initiatives. In this role, you will leverage technologies such as Apache Spark, Airflow, and Python to build high performance data processing systems and ensure data quality, reliability, and lineage across Mastercard's data ecosystem. The ideal candidate combines strong technical expertise with hands-on experience in distributed data systems, workflow automation, and performance tuning to deliver impactful, data-driven solutions at enterprise scale. Responsibilities: Design and optimize Spark-based ETL pipelines for large-scale data processing. Build and manage Airflow DAGs for scheduling, orchestration, and checkpointing. Implement partitioning and shuffling strategies to improve Spark performance. Ensure data lineage, quality, and traceability across systems. Develop Python scripts for data transformation, aggregation, and validation. Execute and tune Spark jobs using spark-submit. Perform DataFrame joins and aggregations for analytical insights. Automate multi-step processes through shell scripting and variable management. Collaborate with data, DevOps, and analytics teams to deliver scalable data solutions. Qualifications: Bachelor's degree in Computer Science, Data Engineering, or related field (or equivalent experience). At least 7 years of experience in data engineering or big data development. Strong expertise in Apache Spark architecture, optimization, and job configuration. Proven experience with Airflow DAGs using authoring, scheduling, checkpointing, monitoring. Skilled in data shuffling, partitioning strategies, and performance tuning in distributed systems. Expertise in Python programming including data structures and algorithmic problem-solving. Hands-on with Spark DataFrames and PySpark transformations using joins, aggregations, filters. Proficient in shell scripting, including managing and passing variables between scripts. Experienced with spark submit for deployment and tuning. Solid understanding of ETL design, workflow automation, and distributed data systems. Excellent debugging and problem-solving skills in large-scale environments. Experience with AWS Glue, EMR, Databricks, or similar Spark platforms. Knowledge of data lineage and data quality frameworks like Apache Atlas. Familiarity with CI/CD pipelines, Docker/Kubernetes, and data governance tools.

We envision a world where novel drugs and therapies reach patients in months, not years, accelerating breakthroughs that save lives. Mithrl is building the world's first commercially available AI Co-Scientist-a discovery engine that empowers life science teams to go from messy biological data to novel insights in minutes. Scientists ask questions in natural language, and Mithrl answers with real analysis, novel targets, and patent-ready reports. No coding. No waiting. No bioinformatics bottlenecks. We are the fastest growing tech-bio startup in the Bay Area with over 12X YoY revenue growth. Our platform is already being used by teams at some of the largest biotechs and big pharma across three continents to accelerate and uncover breakthroughs-from target discovery to mechanism of action. WHAT YOU WILL DO Build and own an AI-powered ingestion & normalization pipeline to import data from a wide variety of sources - unprocessed Excel/CSV uploads, lab and instrument exports, as well as processed data from internal pipelines. Develop robust schema mapping, coercion, and conversion logic (think: units normalization, metadata standardization, variable-name harmonization, vendor-instrument quirks, plate-reader formats, reference-genome or annotation updates, batch-effect correction, etc.). Use LLM-driven and classical data-engineering tools to structure “semi-structured” or messy tabular data - extracting metadata, inferring column roles/types, cleaning free-text headers, fixing inconsistencies, and preparing final clean datasets. Ensure all transformations that should only happen once (normalization, coercion, batch-correction) execute during ingestion - so downstream analytics / the AI “Co-Scientist” always works with clean, canonical data. Build validation, verification, and quality-control layers to catch ambiguous, inconsistent, or corrupt data before it enters the platform. Collaborate with product teams, data science / bioinformatics colleagues, and infrastructure engineers to define and enforce data standards, and ensure pipeline outputs integrate cleanly into downstream analysis and storage systems. WHAT YOU BRING Must-have 5+ years of experience in data engineering / data wrangling with real-world tabular or semi-structured data. Strong fluency in Python, and data processing tools (Pandas, Polars, PyArrow, or similar). Excellent experience dealing with messy Excel / CSV / spreadsheet-style data - inconsistent headers, multiple sheets, mixed formats, free-text fields - and normalizing it into clean structures. Comfort designing and maintaining robust ETL/ELT pipelines, ideally for scientific or lab-derived data. Ability to combine classical data engineering with LLM-powered data normalization / metadata extraction / cleaning. Strong desire and ability to own the ingestion & normalization layer end-to-end - from raw upload → final clean dataset - with an eye for maintainability, reproducibility, and scalability. Good communication skills; able to collaborate across teams (product, bioinformatics, infra) and translate real-world messy data problems into robust engineering solutions. Nice-to-have Familiarity with scientific data types and “modalities” (e.g. plate-readers, genomics metadata, time-series, batch-info, instrumentation outputs). Experience with workflow orchestration tools (e.g. Nextflow, Prefect, Airflow, Dagster), or building pipeline abstractions. Experience with cloud infrastructure and data storage (AWS S3, data lakes/warehouses, database schemas) to support multi-tenant ingestion. Past exposure to LLM-based data transformation or cleansing agents - building or integrating tools that clean or structure messy data automatically. Any background in computational biology / lab-data / bioinformatics is a bonus - though not required. WHAT YOU WILL LOVE AT MITHRL Mission-driven impact: you'll be the gatekeeper of data quality - ensuring that all scientific data entering Mithrl becomes clean, consistent, and analysis-ready. You'll have outsized influence over the reliability and trustworthiness of our entire data + AI stack. High ownership & autonomy: this role is yours to shape. You decide how ingestion works, define the standards, build the pipelines. You'll work closely with our product, data science, and infrastructure teams - shaping how data is ingested, stored, and exposed to end users or AI agents. Team: Join a tight-knit, talent-dense team of engineers, scientists, and builders Culture: We value consistency, clarity, and hard work. We solve hard problems through focused daily execution Speed: We ship fast (2x/week) and improve continuously based on real user feedback Location: Beautiful SF office with a high-energy, in-person culture Benefits: Comprehensive PPO health coverage through Anthem (medical, dental, and vision) + 401(k) with top-tier plans