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Data modeler skills for your resume and career

Updated January 8, 2025

5 min read

Quoted experts

Yingfu (Frank) Li Ph.D.,

Michael Gallaugher Ph.D.

Below we've compiled a list of the most critical data modeler skills. We ranked the top skills for data modelers based on the percentage of resumes they appeared on. For example, 6.1% of data modeler resumes contained etl as a skill. Continue reading to find out what skills a data modeler needs to be successful in the workplace.

15 data modeler skills for your resume and career

1. ETL

Here's how data modelers use etl:

Analyzed ETL processing to identify risk factors and made necessary recommendations to ensure data integrity in the data warehouse.
Worked to ensure implementation meets the documented specifications for ETL processes including data translation/mapping and transformation.

2. Data Analysis

Here's how data modelers use data analysis:

Redefined many attributes and relationships in the reverse engineered model and cleansed unwanted table/columns as part of data analysis responsibility.
Reviewed Entities and relationships in the engineered model and cleansed unwanted tables/ columns as part of data analysis responsibilities.

3. Data Architecture

Here's how data modelers use data architecture:

Maintained and enforce data architecture/administration standards, as well as standardization of column name abbreviations, domains and attributes.
Performed an analysis of U.S. Customs Automated Commercial System's as-is architecture to establish baseline data architecture.

4. Physical Data Models

Here's how data modelers use physical data models:

Performed Reverse Engineering of the current application using Erwin, and developed Logical and Physical data models for Central Model consolidation.
Developed the logical data models and physical data models that confine existing condition/potential status data fundamentals and data flows using Erwin.

5. Data Warehouse

Data warehouse, often abbreviated as either DW or DWH is a system used in computing for data analysis as well reporting. The DW is also considered to be an integral component of business intelligence as they also provide storage facilities for both real-time and historical data. ETL and ELT are the two driving forces behind a data warehouse system.

Here's how data modelers use data warehouse:

Participated with key management resources in the strategic analysis and planning requirements for Data Warehouse/Data Mart reporting and data mining solutions.
Designed and developed system for generating data warehouse quality test scripts driven by meta-data extracted from ER/Studio models.

6. Tableau

Here's how data modelers use tableau:

Developed and delivered dynamic reporting visuals using Tableau.
Used various reporting objects like Hierarchies, filters, calculated fields, Sets, Groups, Parameters etc., in Tableau.

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7. Metadata

Metadata is a combination of two words, meta and data. Meta is a prefix defined as more comprehensive, and data means collection of data. Metadata is defined as the data that describes other data. It is used to summarize data about data to make the data easier to analyze.

Here's how data modelers use metadata:

Developed Private Bank's Metadata Strategy that leverages shared corporate resources including use of Computer Associates Advantage Metadata Repository.
Created SQL queries to extract Report and source/target metadata from Business Objects and Oracle Data Integrator internal repository databases.

8. SQL Server

Here's how data modelers use sql server:

Created custom data repository application and database using SQL Server.
Performed various SQL Server data administration functions.

9. Visualization

Here's how data modelers use visualization:

Translate business requirements into functional design specifications and used visualization tool to get consensus from business users during development.
Developed complex algorithms and advanced (undocumented) tableau visualization techniques that were successfully integrated into Tableau dashboard models.

10. BI

Here's how data modelers use bi:

Modified logical and physical relational and dimensional models for sales analysis BI reporting.
Interacted with BI developers for business requirements gathering to generate the report elements.

11. Extraction

Here's how data modelers use extraction:

Performed GIS data manipulation, analysis, extraction and regeneration with Access and Excel 2.
Involved with Data Extraction, Modification, Validation, Analysis, Management and Reporting.

12. Er Studio

Here's how data modelers use er studio:

Created the dimensional logical model with approximately 10 facts, 30 dimensions with 500 attributes using ER Studio.
Performed ER Studio Repository Administrative tasks assigning Read/write access to models to different users based on their needs.

13. Java

Java is a widely-known programming language that was invented in 1995 and is owned by Oracle. It is a server-side language that was created to let app developers "write once, run anywhere". It is easy and simple to learn and use and is powerful, fast, and secure. This object-oriented programming language lets the code be reused that automatically lowers the development cost. Java is specially used for android apps, web and application servers, games, database connections, etc. This programming language is closely related to C++ making it easier for the users to switch between the two.

Here's how data modelers use java:

Developed interface pages in Java script by facilitating the user to pass parameters into the report.
Helped Java Developers AND Testers in understanding the Data Model.

14. Data Quality

Here's how data modelers use data quality:

Proposed an approach to the consolidation of policy holder and producer information and to data quality improvement.
Maintained the integrity of data also monitored data quality.

15. Profiling

Here's how data modelers use profiling:

Analyzed functional and non-functional data elements for data profiling and mapping from source to target data environment.
Performed data profiling, attribute analysis.

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What skills help Data Modelers find jobs?

Tell us what job you are looking for, we’ll show you what skills employers want.

What skills stand out on data modeler resumes?

Yingfu (Frank) Li Ph.D.

Program Chair of Statistics and Associate Professor of Statistics, University of Houston - Clear Lake

Statistical computing and communication skills

What soft skills should all data modelers possess?

Michael Gallaugher Ph.D.

Assistant Professor, Elected Director of The Classification Society, Baylor University

From the beginning, statistics have been very interdisciplinary and have become even more so in recent years. With that comes working with people with various backgrounds, including those who have only a very basic understanding of mathematics and statistics. Therefore, a statistician needs to reduce the mathematical and computational jargon to simple language.

What hard/technical skills are most important for data modelers?

Michael Gallaugher Ph.D.

Assistant Professor, Elected Director of The Classification Society, Baylor University

With the types of data being analyzed today, computational and coding skills are key. Anyone entering the statistics field, regardless of going into academia or industry, should be comfortable coding in at least one statistical computing language such as R, python, or more recently, Julia. In addition, and this is probably obvious, strong mathematical skills are also very important.

What data modeler skills would you recommend for someone trying to advance their career?

Edward Boone Ph.D.

Professor of Statistics, Virginia Commonwealth University

If a student is graduating from high school, I would recommend taking a gap year or a very light year. Take the extra time to determine what you love to do. I always tell my students, "Do what you love, and people will see your passion for it and then pay you to do that!" From my personal experience, knowing what you want to do in life is not apparent when you are young. Take the time to figure out what you enjoy doing.

I see many students who say they want to be a medical doctor. I usually respond to them, "You want to work with sick people the rest of your life?" Some will say "yes" that they want to make a difference and help sick people. Others will say, "I hadn't thought about it."

In terms of skills...

If someone is interested in a technical career such as Statistics, Mathematics, Operations Research, Data Science, Computer Science, Engineering, I would learn a computer language. Even if you already know one computer language, discover another. All of the above careers require one to be able to learn new computer languages quickly.

The other immense skill someone should learn is interpersonal skills. Often technical people spend a large portion of their job sitting in front of a computer. However, interacting with people is the most critical skill, as you will always need to work with others.

What type of skills will young data modelers need?

Zhixin Wu Ph.D.

Associate Professor, DePauw University

Problem solving skills, analytical skills, self-learning ability, and good communication skills.

Do you want to know more?

List of data modeler skills to add to your resume

The most important skills for a data modeler resume and required skills for a data modeler to have include:

ETL
Data Analysis
Data Architecture
Physical Data Models
Data Warehouse
Tableau
Metadata
SQL Server
Visualization
BI
Extraction
Er Studio
Java
Data Quality
Profiling
Data Governance

Data Warehousing
SAS
Schema
DDL
Snowflake
XML
Data Dictionary
Data Marts
DB2
Oracle Sql
Dimensional Data
OLTP
Regression
Logical Data Model
Strong Analytical
EDW

Data Elements
Business Analysts
PL/SQL
Business Rules
QA
Toad
Business Process
Linux
Unix
Reverse Engineering
SME
Ssis
UML
ODS
Sap Hana
OLAP

Updated January 8, 2025

Zippia Team

Editorial Staff

The Zippia Research Team has spent countless hours reviewing resumes, job postings, and government data to determine what goes into getting a job in each phase of life. Professional writers and data scientists comprise the Zippia Research Team.

Updated January 8, 2025

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