Data and analytics · Mid level · UK and US conventions noted

Data engineer: resume example, explained

This fictional example shows a data engineer with four years' experience applying for a mid-level data platform role: pipelines described by what they feed and how often they fail, warehousing and orchestration tied to the teams that depend on them, and reliability and cost work given concrete figures. Each decision is explained, four weak bullets are rewritten from the evidence behind them, and the editable download lets you replace every detail with your own. Nothing here describes a real person.

Illustrative example. An illustrative, invented data engineer with four years on a fictional logistics marketplace's data platform, applying for a mid-level platform role at a fictional healthcare-scheduling software company. Every employer, system, figure and date is fictional.

Editable version of this example (.docx)

Tomasz Wierzbicki

Data Engineer · batch and streaming pipelines, warehouse modelling, orchestration · 4 years

Bristol, UK (remote-friendly) · 07700 900 318 · tomasz.wierzbicki@example.com

Profile

Data engineer with four years on a fictional logistics marketplace's data platform, owning around 60 scheduled pipelines that land shipment, pricing and billing data in a cloud warehouse for 30 analysts and two finance teams. Introduced data contracts and freshness checks that cut pipeline incidents from about eight a month to two, and led a cost review that reduced warehouse compute spend by roughly a quarter. Seeking more ownership of streaming and modelling standards.

Experience

Data Engineer, Data Platform — Fictional Freight Marketplace Ltd

January 2024 – present · Platform team of five engineers; cloud warehouse of around 12 TB; Airflow orchestration; dbt for transformations.

Own around 60 Airflow-scheduled pipelines landing shipment, pricing and billing events every hour, used by 30 analysts and the month-end billing run.
Agreed data contracts with three producing service teams for 14 core event types, covering schema, ownership and change notice; breaking changes reaching the warehouse fell from roughly monthly to none over the following two quarters.
Added freshness, volume and null-rate checks to every critical dbt model with alerts routed to the owning team; pipeline incidents fell from about eight a month to two, and the billing run has not been delayed by late data since.
Cut monthly warehouse compute spend by roughly a quarter, with no change to report timings, by clustering the five largest tables, auto-suspending two idle clusters and rewriting four runaway queries.
Wrote the team's runbook and on-call guide (around 40 pages); a new joiner handled the overnight rota alone within six weeks.

Junior Data Engineer — Fictional Freight Marketplace Ltd

September 2022 – January 2024

Migrated 25 stored-procedure transformations into version-controlled dbt models with tests, bringing the transformation layer under code review for the first time.
Built the first streaming consumer for live shipment-status events, landing them within two minutes of emission for the customer-facing tracking page.

Education

BSc Computer Science, Fictional University, 2022
Cloud data platform certification (vendor foundation level), 2024

Writing and talks

Internal guide, 'Data contracts for event producers' (2025), adopted as the platform team's standard template.
Team talk, 'What eight incidents a month were teaching us' (2024), the retrospective that led to the freshness checks.

Skills

Python and SQL (daily: pipeline code, dbt models, query tuning) · Airflow orchestration (DAG design, backfills, SLAs) · dbt (incremental models, tests, documentation) · Cloud data warehouse administration (clustering, cost controls, access roles) · Streaming ingestion (Kafka consumers, idempotent writes) · Data contracts, schema evolution, data-quality monitoring · Infrastructure as code (Terraform) and CI for pipelines

Why it is written this way

Pipelines are described by what depends on them, not by the stack

A data engineer resume can collapse into a tool list: Airflow, dbt, a warehouse, a streaming platform. This one names the tools but leads with what each pipeline feeds and who waits for it: 30 analysts, a month-end billing run, a customer-facing tracking page. A platform hiring manager is judging whether the applicant understands the consequences of late or wrong data, which the stack alone cannot show.

Reliability gets a before-and-after number

Incidents falling from about eight a month to two is the central claim of the resume, and it is deliberately modest. Data engineers are hired to make data arrive on time and correct, so monitoring, alerting and contracts are shown with the failure rate they changed rather than as 'improved reliability'. A billing run no longer delayed turns an engineering metric into something a finance director notices.

Cost work is explained as decisions, not savings

The cost bullet lists what was actually done: clustering the largest tables, auto-suspending idle clusters, rewriting runaway queries. A proportion saved with no method would be unverifiable, and the method is what a data engineer interviewer asks about. 'With no change to report timings' matters because a cost cut that slows analysts down is not an achievement.

Documentation and data contracts count as engineering

A runbook that let a new joiner take the rota, and contracts agreed with producing teams, sit alongside the pipeline work rather than in a soft-skills line. At mid level a data engineer is expected to build systems that others can run and change safely; the resume shows that with a time-to-independence figure and a count of contracted event types.

Bullet rewrites, and the evidence behind them

Each rewrite was produced by asking an evidence question and using only the (fictional) answer. No number appears in a rewrite that is not in its evidence.

Before
Built and maintained ETL pipelines.
Evidence question
How many pipelines, how often do they run, what do they land, and who uses the output?
Evidence (fictional)
Around 60 Airflow-scheduled pipelines; hourly; shipment, pricing and billing events; 30 analysts and the month-end billing run.
After
Own around 60 Airflow-scheduled pipelines landing shipment, pricing and billing events every hour, used by 30 analysts and the month-end billing run.
Before
Improved data quality and reliability.
Evidence question
Which checks did you add, where do alerts go, and what did the failure rate do?
Evidence (fictional)
Freshness, volume and null-rate checks on every critical dbt model; alerts routed to the owning team; incidents fell from about eight a month to two.
After
Added freshness, volume and null-rate checks to every critical dbt model with alerts routed to the owning team; pipeline incidents fell from about eight a month to two.
Before
Reduced cloud costs.
Evidence question
What did you change, by how much did spend fall, and what did it cost the users?
Evidence (fictional)
Clustered the five largest tables, moved two always-on clusters to auto-suspend, rewrote four runaway queries; monthly compute spend down by roughly a quarter; report timings unchanged.
After
Cut monthly warehouse compute spend by roughly a quarter, with no change to report timings, by clustering the five largest tables, auto-suspending two idle clusters and rewriting four runaway queries.
Before
Worked with other teams on data schemas.
Evidence question
What agreement did you reach, covering how many event types, and what stopped happening?
Evidence (fictional)
Data contracts with three producing service teams covering 14 core event types: schema, ownership, change notice; breaking schema changes fell from roughly monthly to none over the following two quarters.
After
Agreed data contracts with three producing service teams for 14 core event types, covering schema, ownership and change notice; breaking changes reaching the warehouse fell from roughly monthly to none over the following two quarters.

Try it on your own bullets with the bullet workbench, which asks the same questions and never adds a figure you did not supply.

Transferable skills in this example

Running production systems on an on-call rota
Evidence: Owns a 60-pipeline estate, wrote the runbook and trained a new joiner to take the overnight rota.
Where it helps: Site reliability, platform engineering and any operations role where incidents are counted and reduced.
Negotiating interfaces between teams
Evidence: Agreed data contracts with three producing teams, including change-notice periods.
Where it helps: Integration engineering, API product work and technical programme management.
Cost-conscious engineering
Evidence: A cost review that cut compute spend by roughly a quarter without slowing users.
Where it helps: Cloud and FinOps roles, infrastructure teams and any engineering position with a budget line.
Writing that lets others operate a system
Evidence: A 40-page runbook and an internal data-contracts guide adopted as the team template.
Where it helps: Developer-experience, technical-writing and staff-engineer roles.

Occupation notes

Hard requirements

Right to work in the role's country is checked through the verified employer process at the stage required in that country and is not resume content; if the advert asks about sponsorship, answer on the form or in the cover letter.
Data engineering adverts typically name the warehouse, orchestrator and language the team uses as must-haves; list the ones you have run in production, because a take-home or live SQL and Python exercise usually follows. A degree is a hard requirement only where the advert says so; this fictional advert asked for Python, SQL, an orchestrator and a cloud warehouse, not a qualification.

Vocabulary that belongs on the page

Orchestration, DAG, backfill, idempotent task (Airflow and similar schedulers)
Incremental model, test, documentation (the dbt transformation layer)
Data contract, schema evolution, breaking change, producer and consumer teams
Freshness, volume and null-rate checks; SLA on data arrival
Clustering and partitioning, auto-suspend, compute credits, query tuning
Streaming consumer, event, at-least-once versus exactly-once delivery
Runbook, on-call rota, incident, postmortem
Layered (medallion) modelling, fact and dimension tables, slowly changing dimensions

Pitfalls specific to this occupation

A stack list with no system described: twelve tool names tell a data engineer interviewer nothing about what you have run in production or broken and fixed.
Pipelines described without their consumers: 'built ETL pipelines' leaves out who waited for the data and what happened when it was late.
Reliability or cost claims with no method: 'improved performance significantly' invites the question 'how', and a resume that cannot answer it loses trust.
Treating documentation, contracts and on-call as soft skills rather than engineering; at mid level they are the evidence of ownership.

UK and US differences

UK: UK adverts say 'data engineer' or 'analytics engineer'; the latter usually means the dbt and modelling end of the work. Two pages is normal for four years' experience.

US: US resumes for this work run to one page at mid level, drop the photo and personal details, and often state the cloud provider in the headline. Technical screens in SQL and Python are standard, so list only what you can do live.

Common questions

Should a data engineer resume list every tool in the stack?

List the tools you have used in production and could be tested on. Put the version-controlled, orchestrated, monitored parts of your work in bullets with figures, and leave out tools you only touched in a tutorial.

How do I show reliability work without exposing incident details?

Give the rate before and after, the checks or contracts you introduced and the downstream effect, such as a billing run no longer delayed. You do not need to name the systems that failed or the customers affected.

Published 6 November 2026 · Every person, employer and figure in this example is fictional. Drafted with AI assistance and checked against Luceria’s editorial standards.