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Etl developer vs data scientist

The differences between etl developers and data scientists can be seen in a few details. Each job has different responsibilities and duties. It typically takes 2-4 years to become both an etl developer and a data scientist. Additionally, a data scientist has an average salary of $106,104, which is higher than the $92,419 average annual salary of an etl developer.

The top three skills for an etl developer include sql server, data warehouse and unix. The most important skills for a data scientist are python, data science, and visualization.

Etl developer vs data scientist overview

ETL DeveloperData Scientist
Yearly salary$92,419$106,104
Hourly rate$44.43$51.01
Growth rate21%16%
Number of jobs88,652106,973
Job satisfaction--
Most common degreeBachelor's Degree, 76%Bachelor's Degree, 51%
Average age3941
Years of experience44

What does an etl developer do?

An ETL developer is responsible for managing data storage systems to secure the organization's data and files for daily operations efficiency. ETL developers perform multiple system testing to ensure the system's accuracy, perform coding adjustments, and troubleshoot for any defects and inconsistencies. They work closely with other development teams to design storage functions to optimize solutions. An ETL developer must have extensive knowledge of the technology industry and a strong command of programming languages to develop an accurate and operational database.

What does a data scientist do?

A Data Scientist analyzes information from multiple sources in order to gain maximum insight that can give the company a competitive advantage. They work in different domains, including manufacturing, healthcare, education, and finance.

Etl developer vs data scientist salary

Etl developers and data scientists have different pay scales, as shown below.

ETL DeveloperData Scientist
Average salary$92,419$106,104
Salary rangeBetween $72,000 And $118,000Between $75,000 And $148,000
Highest paying CitySan Francisco, CARichmond, CA
Highest paying stateWashingtonCalifornia
Best paying companyMetaThe Citadel
Best paying industryHealth CareStart-up

Differences between etl developer and data scientist education

There are a few differences between an etl developer and a data scientist in terms of educational background:

ETL DeveloperData Scientist
Most common degreeBachelor's Degree, 76%Bachelor's Degree, 51%
Most common majorComputer ScienceComputer Science
Most common collegeMassachusetts Institute of TechnologyColumbia University in the City of New York

Etl developer vs data scientist demographics

Here are the differences between etl developers' and data scientists' demographics:

ETL DeveloperData Scientist
Average age3941
Gender ratioMale, 68.0% Female, 32.0%Male, 79.6% Female, 20.4%
Race ratioBlack or African American, 4.3% Unknown, 4.7% Hispanic or Latino, 8.0% Asian, 35.2% White, 47.7% American Indian and Alaska Native, 0.2%Black or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%
LGBT Percentage8%9%

Differences between etl developer and data scientist duties and responsibilities

Etl developer example responsibilities.

  • Support and maintain production Cognos web portal to manage the OLAP cube and folders.
  • Implement Perl scripts to load the financial information and to automate frequent reports for the users.
  • Coordinate with DBA in creating and managing table, indexes, table spaces, triggers, dB links and privileges.
  • Migrate the jobs from DataStage 8.1 to 8.7 and the environment from AIX to LINUX.
  • Export data from HDFS environment into RDBMS using Sqoop for report generation and visualization purpose.
  • Develop PL/SQL package for conversion of legacy notes associate with a service request using service request API.
  • Show more

Data scientist example responsibilities.

  • Update, maintain, and manage regional CRM database and records for customers, vendors, and suppliers.
  • Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
  • Lead the analysis in SAS for data integration of mortality data using meta-analysis integration methods.
  • Implement a proximal stochastic gradient descent with a line search to fit a regularize logistic regression in Scala
  • Perform cross-validation-test on linear regression model of data using scikit-learn.
  • Develop python base statistical visualization to provide insights of fuzzy social media data.
  • Show more

Etl developer vs data scientist skills

Common etl developer skills
  • SQL Server, 7%
  • Data Warehouse, 6%
  • Unix, 5%
  • Data Analysis, 4%
  • BI, 4%
  • Data Quality, 4%
Common data scientist skills
  • Python, 13%
  • Data Science, 10%
  • Visualization, 5%
  • Java, 4%
  • Hadoop, 4%
  • Tableau, 3%

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