Data Engineer résumé: what employers are asking for right now
We read 106 open data engineer postings on employers' own job boards, at companies including Anthropic, Airbnb and OpenAI. Of the 76 whose requirements we have read, the skills asked for most are SQL (76%), Python (75%) and Data Modeling (53%) — so those belong near the top of a data engineer résumé, in the words the posting uses. The median posting asks for 5+ years of experience.
From 106 open postings on employers’ own job boards, as of .
Skills data engineer postings ask for
| Skill | Share of postings | Bar |
|---|---|---|
| SQL | 76% | |
| Python | 75% | |
| Data Modeling | 53% | |
| dbt | 46% | |
| ETL | 32% | |
| Airflow | 26% | |
| Git | 25% | |
| Snowflake | 25% | |
| Bigquery | 24% | |
| Data Pipelines | 24% | |
| Distributed Systems | 21% | |
| Spark | 18% | |
| Data Engineering | 17% | |
| Java | 17% | |
| Kubernetes | 14% |
Share of the 76 postings whose requirements we have read.
A skills section built from those postings
List only what you can talk about in an interview, and use the posting’s own words for it — a screener searching for “SQL” will not match a synonym. Ordered by how often employers ask:
Skills
SQL · Python · Data Modeling · dbt
ETL · Airflow · Git · Snowflake · Bigquery · Data Pipelines · Distributed Systems · Spark · Data Engineering · Java
Kubernetes
- Line 1: asked for by a third or more of postings.
- Line 2: often asked for.
- Line 3: sometimes asked for.
Sample data engineer résumé
An illustrative example, not a real person: the employers and results are made up. The skills are the ones the postings above ask for, so it shows how those requirements read on a page.
Senior Data Engineer
Senior Data Engineer with 5+ years building SQL and Python data platforms, ETL workflows, and analytics models. Experience spans dbt, Airflow, Snowflake, Bigquery, and distributed systems, with a focus on reliable data pipelines and measurable performance gains.
Experience
Senior Data Engineer · Fast-growing SaaS analytics company
2021 present
- Built SQL and Python data pipelines for product and revenue reporting, cutting daily refresh time from 6 hours to 75 minutes.
- Designed dimensional data modeling standards in dbt and Snowflake, improving metric consistency across 12 business dashboards.
- Orchestrated ETL workflows in Airflow with Git-based review and release practices, reducing failed runs by 40%.
- Scaled Spark jobs on Kubernetes-backed infrastructure to process 8x more event data without increasing the on-call load.
Data Engineer · Regional digital marketplace
2019 2021
- Developed Bigquery-based datasets and SQL transformations for marketplace analytics, replacing manual spreadsheet workflows.
- Implemented Python data pipelines for customer, order, and payments data, improving freshness from next-day to near-real-time.
- Partnered on data engineering migration work to standardize tables, tests, and documentation across core domains.
- Optimized distributed systems jobs and partitioning logic, lowering compute costs by 28% while keeping SLAs stable.
Skills
SQL · Python · Data Modeling · dbt · ETL · Airflow · Git · Snowflake · Bigquery · Data Pipelines · Distributed Systems · Spark
More data engineer bullet points to adapt
- Built governed data models for finance and product teams, making shared metrics consistent across self-serve dashboards.
- Automated ETL validation checks in dbt and Python, catching schema drift before it reached downstream reports.
- Refactored Airflow DAGs to reduce task retries and improve lineage visibility for critical pipelines.
- Migrated legacy warehouse logic into Snowflake and Bigquery, simplifying maintenance and improving query performance.
- Created reusable SQL patterns for incremental loads, slowly changing dimensions, and late-arriving events.
- Packaged Spark transformations for batch processing of high-volume clickstream data with stable runtime and recovery.
- Introduced Git-based code review for pipeline changes, improving deployment safety and auditability.
- Coordinated data engineering support for new product launches, ensuring reporting was available on day one.
What sets an application apart
Listed as a plus rather than a requirement: GCP (14%), AWS (12%), Machine Learning (12%), Kubernetes (11%), Airflow (9%), dbt (9%), LLM (9%) and AI (8%). Worth naming if you have it, and never worth claiming if you do not.
Level, location and pay
- Most are senior roles (51% of postings that state a level).
- 37% are listed as remote.
- Listed mainly in United States, Canada and Japan.
- US postings that publish a yearly salary: median $215k, middle half $198k–$298k (from 19 postings).