Data
Data Engineer CV: what employers ask for
Data engineers are judged on the pipelines and platforms that other teams rely on. Screeners want to know what data you moved, how much, how reliably and who used it. A list of tools without volumes, freshness or consumers is hard to assess.
From real postings
We read 55 Data Engineer job postings from 37 companies on 6 November 2026. The most named asks were Python (41), SQL (40) and Data pipelines (38). 36 postings stated years of experience; the middle value was 3.5.
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What a strong Data Engineer CV looks like
Start here if you are editing a CV today. The evidence for each point follows below.
Section order, top to bottom
- HeaderName, email, phone, city, and a link only if it shows real data work, such as a repository with a pipeline and tests.
- SummaryTwo or three lines: your title, years of experience, the kind of data you move (batch, streaming, or both), and your core stack.
- ExperienceMost recent first. Each bullet should say what data, how much, how often, how fresh, and who used it.
- SkillsGrouped: languages (SQL, Python), processing (Spark, dbt), orchestration (Airflow, Dagster), storage (Snowflake, BigQuery), cloud. Keep only what you can discuss.
- ProjectsUseful when moving in from analysis or software engineering. One end-to-end pipeline, with sources, schedule, tests and the table it produces.
- Education and certificationsDegree, then cloud data certifications if the posting names them. They support experience; they do not replace it.
The summary
Lead with what you build and who relies on it. Data modelling and data quality were each named in more than half the postings we read, so if those are strengths, say so here.
Data engineer with four years of experience building batch and streaming pipelines in Python and SQL. I model warehouse data for analysts and finance in dbt on Snowflake, with tests and alerts on the tables people use most.
Skills: show them, do not just list them
SQL and Python were named in most postings, so they are expected, not distinguishing. What separates CVs is evidence around them: volumes, schedules, freshness, consumers and the checks you added. dbt was often listed only as a nice to have, so it helps but rarely decides. Data modelling, data quality and working with stakeholders each came up more often than any tool apart from SQL and Python.
Weak and strong bullets
Illustrative rewrites, not real candidates. Use your own work, and only numbers you can back up.
Weak: Built ETL pipelines.
Stronger: Built Airflow pipelines loading about 50 million events a day from Kafka into Snowflake, available to analysts within 15 minutes.
Why it works: Volume, frequency, freshness and the consumer, all in one line. Those are the four things a reviewer looks for.
Weak: Used dbt for transformations.
Stronger: Rebuilt 60 reporting models in dbt with tests on key columns, ending the monthly mismatch between finance and product revenue figures.
Why it works: Shows data modelling and data quality together, and names the business problem it solved.
Weak: Improved pipeline performance.
Stronger: Repartitioned a Spark job and removed a full-table join, cutting runtime from 4 hours to 35 minutes and halving its compute cost.
Why it works: Says what you changed, not only the result. A reviewer can ask about it in an interview.
Weak: Worked with stakeholders on data needs.
Stronger: Agreed definitions for active customers and churn with finance and product, then built the shared tables both teams now report from.
Why it works: Stakeholder work was a common ask. This shows it as an outcome, not a soft skill.
What Data Engineer job postings ask for
Counted from 55 real postings, not guessed. When you paste a posting into Resymetry, it checks each of these asks against your CV and marks it as shown, only listed, or missing.
| Skill or ask | Postings naming it | Only as nice to have |
|---|---|---|
| Python | 41 of 55 | 1 |
| SQL | 40 of 55 | 1 |
| Data pipelines | 38 of 55 | 3 |
| Data modelling | 34 of 55 | – |
| Data quality / governance | 33 of 55 | 1 |
| Stakeholder management | 31 of 55 | 1 |
| Airflow / orchestration | 30 of 55 | 3 |
| Cross-functional work | 25 of 55 | – |
| ETL / ELT | 24 of 55 | 1 |
| Spark | 24 of 55 | 4 |
Show 8 more
| Skill or ask | Postings naming it | Only as nice to have |
|---|---|---|
| Monitoring / observability | 23 of 55 | 1 |
| AWS | 22 of 55 | 2 |
| dbt | 22 of 55 | 8 |
| GCP | 21 of 55 | 3 |
| Streaming | 21 of 55 | 5 |
| Metrics / KPIs | 20 of 55 | – |
| Machine learning | 16 of 55 | – |
| Snowflake | 15 of 55 | – |
How we counted
On 6 November 2026 we read 55 live Data Engineer postings from 37 companies, through their public job boards (Greenhouse, Lever and Ashby). A posting counted when its title matched the role. We took at most three per company, so no single employer sets the result.
A skill counts once per posting, when it is named outside the benefits and company boilerplate. Matching is by keyword, so read the numbers as “named in”, not “strictly required”. “Only as nice to have” means it appeared only under a heading such as “Nice to have” or “Bonus”. 35 of the 55 postings had such a heading, so that column is a floor.
By location on the posting: 31 US, 3 UK, 5 India, 16 elsewhere or remote. Most employers in the sample are technology companies, which skews the result towards their stacks.
Companies sampled: Airbnb, Anduril, Asana, Bloomreach, Brex, Chime, Cloudflare, Cohere, Coinbase, Cursor, Databricks, Discord, Elastic, ElevenLabs, Fampay, Figma, Gemini, GitLab, GoCardless, Graphcore, HackerRank, InMobi, Lyft, Notion, Okta, OpenAI, Pinterest, Redwood Materials, Riot Games, Robinhood, Samsara, Scale AI, SoFi, SpaceX, Spotify, Stripe, Supabase.
The script that produced these numbers is scripts/role-postings.mjs in the Resymetry repository. It stores counts only, never the text of a posting.
What employers look for in a Data Engineer CV
Screeners check these first. Each one should be visible in your job entries, not only in a skills list.
- Strong SQL and Python, used to build production pipelines rather than one-off scripts.
- Batch or streaming pipelines you built, with volume, schedule and reliability.
- Warehouse or lakehouse experience such as Snowflake, BigQuery, Redshift or Databricks.
- Orchestration and transformation tools like Airflow, Dagster or dbt.
- Data quality practices: tests, monitoring, lineage and handling schema changes.
5 common Data Engineer CV mistakes and how to fix them
Pipelines described without size or frequency.
Fix: Add rows or GB per day, how often it runs and how fresh the data is.
No mention of who used the data.
Fix: Name the consumers: analysts, a product feature, finance reporting or a machine learning model.
Leaving out data quality work.
Fix: Mention tests, alerts or checks you added and what problems they caught. It was among the most named asks.
Reading like a data analyst CV.
Fix: Lead with building and running data systems, not dashboards. Move reporting work lower or describe the data behind it.
Every tool in the modern data stack listed with equal weight.
Fix: Keep the tools you used in production and put them inside bullets. A long list invites questions about the ones you only tried.
Starting from scratch, or the layout is the problem rather than the content? There are free ATS CV templates to start from.
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Data Engineer CV questions
- What should a data engineer CV include?
- A short summary of the data you build for, experience bullets with volume, frequency and consumers, a grouped skills block, and education. Add one end-to-end project if your jobs do not show pipeline work yet.
- What is the difference between a data engineer and a data analyst CV?
- A data engineer CV leads with systems: pipelines, models, reliability and scale. A data analyst CV leads with questions answered and decisions changed. If you are moving across, rewrite bullets around what you built rather than what you reported.
- How important is SQL for data engineering roles?
- Very. SQL and Python were the two most named skills in the postings we read. Show SQL through what it did, such as models, migrations or performance fixes, rather than as a skills-list entry.
- Should I list every tool in the modern data stack?
- No. List the ones you have used for real work, and show the important ones inside your bullets. Breadth matters less than proof that your pipelines ran reliably.
- How do I write a data engineer CV with no experience?
- Build one pipeline end to end: a public data source, an orchestrator, tests, and a table someone could query. Describe it like a job entry, with schedule, size and checks. Link the repository.
- How many years of experience do data engineer postings ask for?
- In our sample, about two in three postings stated a number. The middle value was between three and four years. Treat it as a guide; postings often accept strong evidence over exact years.