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Resume template

Data engineer resume template

This data engineer template is built for ATS parsing with a single text column, standard sections, clear contact details, and quantified bullet points. The sample content highlights batch and streaming pipelines, ETL, Spark, Airflow, Kafka, warehouses, dbt, schema evolution, and data quality ownership.

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Sample resume

Single-column, plain-text structure with standard headings and quantified bullets.

The person, employers, and figures in this sample are illustrative, written to show the format.

Ben Taylor

ben.taylor@example.com+44 20 7946 0106linkedin.com/in/bentaylorgithub.com/bentaylor

Summary

Data engineer with 7 years building reliable batch and streaming pipelines for analytics and ML consumers.

Experienced in Spark, Airflow, Kafka, Snowflake, BigQuery, dbt, data quality, schema evolution, and cost-aware warehouse design.

Experience

Senior Data Engineer

TravelNest | London, UK | February 2022 - Present

  • Lead data platform work for booking, pricing, and marketing datasets totalling 9TB of new data per day.
  • Built Spark and Airflow pipelines that reduced nightly warehouse refresh time from 6 hours to 90 minutes.
  • Implemented schema registry and contract checks that cut downstream breaking changes by 68%.
  • Designed Snowflake cost controls and clustering strategy, reducing monthly compute spend by 24%.
  • Mentor analysts and engineers on dbt modelling, data quality tests, and pipeline ownership.

Data Engineer

ShopMetric | Leeds, UK | May 2018 - January 2022

  • Built Kafka ingestion for clickstream events serving product analytics and recommendation features.
  • Developed dbt marts for orders, customers, and campaigns used by 120 weekly active BI users.
  • Reduced late-arriving event defects by 43% through partitioning, backfill tooling, and freshness checks.
  • Migrated 1.2B rows from Redshift to BigQuery with zero dashboard downtime.

BI Developer

GreenEnergy UK | Newcastle, UK | September 2015 - April 2018

  • Created SQL models and Power BI dashboards for revenue, churn, and field operations.
  • Automated CSV ingestion from 18 vendor systems, saving analysts 16 hours per week.
  • Improved data accuracy for meter readings by 22% through validation and deduplication rules.
  • Documented metric definitions and trained 40 business users on trusted reporting tables.

Skills

Languages

Python, SQL, Scala, Bash

Pipelines

Spark, Airflow, Kafka, Fivetran, dbt

Warehouses

Snowflake, BigQuery, Redshift, Postgres

Quality

Great Expectations, Schema registry, Lineage, Data contracts

Education

  • MSc Data Science, University of Edinburgh
  • BSc Mathematics, University of Leeds

Adapting this template for Data engineer applications

Fit the template to where you sit in the pipeline hierarchy. A BI developer moving into data engineering should keep the reporting history, as the GreenEnergy role does, and add ingestion or orchestration evidence on top, since reviewers want to see the transition already underway. Senior candidates should emphasise the platform bullets: schema contracts, cost controls, and mentoring, because at that level you are hired to stop breakage across teams, not to write one more pipeline.

The first screen on a data engineering resume checks orchestration, streaming, and warehouse names, which is why Spark, Airflow, Kafka, Snowflake, and dbt each appear in a bullet here rather than only in the skills groups. The second screen looks for scale and reliability units: terabytes per day, refresh duration, breaking-change reduction, and spend. Those figures matter because pipeline work is invisible when healthy; numbers are the only way a reviewer can see it.

Typical filters for this role: tool lists that mix everything from Excel to Spark with no hierarchy, which suggests the candidate cannot tell workloads apart; migration claims with no row counts or downtime statement; and no mention of data quality at all. A data engineer who never talks about freshness, contracts, or failed-job rates reads as someone who builds pipelines but has never been paged by one.

ATS notes for Data engineer

  • Include ETL, ELT, Spark, Airflow, Kafka, Snowflake, BigQuery, dbt, and data quality as literal keywords.
  • Quantify data work with terabytes processed, refresh time, job failure rate, cost, and user adoption.
  • Separate warehouse, orchestration, streaming, and quality tools in Skills for cleaner keyword matching.
  • Mention schema evolution, lineage, freshness, and contracts for senior data engineering roles.
  • Use conventional headings because ATS parsers often classify data resumes by Experience, Skills, and Education.

Data engineer resume questions

Batch or streaming: does the resume need both?

Show what you have, but label each clearly. This sample pairs Airflow batch pipelines with Kafka clickstream ingestion so both screening keywords land. If you only have batch experience, do not fake streaming; instead show depth in scheduling, backfills, and late-arriving data, which interviewers respect because it is where batch work gets hard.

How do I show data quality work beyond saying I added tests?

Quantify the failure you prevented. The sample cites contract checks cutting downstream breaking changes by 68% and freshness checks reducing late-event defects. Name the mechanism, whether Great Expectations, dbt tests, or a schema registry, then the defect class it caught. Quality bullets are strongest when framed as protecting the people who consume your tables.

Should warehouse cost optimisation appear on a data engineer resume?

Yes, it is one of the most senior signals available. Compute spend is a first-class engineering concern in every warehouse-heavy company, and few candidates address it. One bullet naming the tactic, such as smarter table layout or incremental refreshes, plus the percentage saved, as this sample does for Snowflake, will get raised in almost every interview.

Continue your prep

Data engineer interview questions

Practise the data engineer questions recruiters use to test role knowledge, judgement, and depth.

Data engineer salary data

Compare compensation bands before negotiation so the resume, interview story, and target range line up.