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Data engineer resume example

A mid-level data engineer’s resume with five years of experience across batch and streaming. Data platform managers read for volume, freshness, reliability and cost, so each bullet pairs a tool with the number it moved.

Data engineer resume example, page 1 of 1Siddhant BhattacharjeeData Engineersiddhant.bhattacharjee@example.com · (720) 555-0153 · Denver, CO · linkedin.com/in/siddhantb-data ·github.com/example-sbhattacharjeeSummaryData engineer with five years building batch and streaming pipelines on AWS and Snowflake for energy andhealthcare companies. I build pipelines that are boring in the best way: tested, monitored, cheap to run andeasy for the next engineer to change.ExperienceData Engineer IIMay 2023 – PresentBitterroot Energy · Denver, CO•Built the smart meter pipeline (Kafka, Spark Structured Streaming, Snowflake) that ingests 1.8 billion readingsa day with 15-minute freshness.•Moved 140 legacy SQL jobs to Airflow and dbt with tests on every model, cutting failed morning loads from 6a month to under 1.•Cut Snowflake spend 34% ($210,000 a year) by clustering large tables, right-sizing warehouses and addingcost alerts per team.•Designed the data contract process between app teams and the warehouse, adopted by 8 producing teams.•On call for the data platform one week in six; wrote the runbooks for the 12 most common failures.Data EngineerAug 2021 – Apr 2023Columbine Health Partners · Denver, CO•Built Python and AWS Glue pipelines loading claims and EHR extracts from 30 clinics into Redshift.•Automated HIPAA-compliant de-identification of patient data for the analytics team, replacing a manualmonthly process.•Added Great Expectations checks to 50 tables, catching a vendor file format change before it reachedreports.EducationB.S. in Computer Science2017 – 2021Colorado School of Mines · Golden, COSkillsPython · SQL · Apache Airflow · dbt · Snowflake · Apache Spark · Kafka · AWS (S3, Glue, Lambda, Redshift) ·Terraform · Docker · Great Expectations · Data modeling · CI/CD (GitHub Actions)CertificationsSnowPro Core Certification2024SnowflakeAWS Certified Data Engineer, Associate2024Amazon Web Services
Data engineerUse this example

What recruiters look for

  • Scale: rows or events a day, terabytes stored, number of sources and tables. It tells a reader what kind of system you have run.
  • Reliability: tests, data quality checks, failed-load rates, on-call. Downstream teams judge data engineers by whether the data is there at 8 a.m.
  • Modern stack fluency: an orchestrator (Airflow, Dagster, Prefect), a modeling layer (dbt), a warehouse or lakehouse, and a cloud.
  • Cost awareness: warehouse and compute savings with dollar figures. Few resumes show it, and every data leader cares.
  • Software engineering habits: version control, CI, infrastructure as code, code review.

Writing the summary

Years, your main cloud and warehouse, batch or streaming (or both), and the domain. Add a line on how you build, if it is specific: tested, documented, cost-aware.

The summary in this example

Data engineer with five years building batch and streaming pipelines on AWS and Snowflake for energy and healthcare companies. I build pipelines that are boring in the best way: tested, monitored, cheap to run and easy for the next engineer to change.

Analytics engineer

Analytics engineer with four years building dbt models on BigQuery for a subscription business. I sit between data engineering and analytics: I own the modeled layer 30 analysts query every day, with tests, documentation and metric definitions finance signs off on.

Software engineer moving into data engineering

Backend engineer with five years in Python and PostgreSQL who has spent the last two building our event pipeline and reporting tables. Moving into a full-time data engineering role on Spark, Airflow and a cloud warehouse.

Bullet points that work

Strong lines for this role, beyond the ones in the example. Borrow the shape and put in your own numbers.

  • Replaced a nightly full reload of a 4 TB table with incremental merges, cutting runtime from 5 hours to 20 minutes.
  • Built a change data capture pipeline with Debezium and Kafka from 12 PostgreSQL databases, bringing warehouse latency from 24 hours to 2 minutes.
  • Wrote Terraform modules for the data platform, letting new pipelines go from request to production in 2 days instead of 2 weeks.
  • Migrated 300 TB from an on-premises Hadoop cluster to Databricks on AWS with no missed reporting deadlines.
  • Introduced data contracts and schema checks in CI, blocking 45 breaking changes before they reached production in a year.
  • Cut BigQuery costs by $15,000 a month by partitioning event tables and rewriting the 20 most expensive queries.
  • Built a backfill framework that reprocessed 3 years of history in a weekend after a source system bug.
  • Set freshness SLAs for 60 critical tables and an alerting dashboard, raising on-time delivery to 99.5%.
  • Onboarded 4 analysts to contributing dbt models through pairing and a written style guide.

Skills to list

Hard skills

  • Python and SQL at production level
  • An orchestrator (Airflow, Dagster or Prefect)
  • dbt and dimensional modeling
  • A warehouse or lakehouse (Snowflake, BigQuery, Redshift, Databricks)
  • Spark for large batch jobs
  • Streaming (Kafka, Kinesis, Flink)
  • A cloud platform (AWS, GCP or Azure)
  • Infrastructure as code (Terraform)
  • Data quality testing (dbt tests, Great Expectations)

Soft skills

  • Designing for the next maintainer
  • Calm on-call
  • Working with analysts as customers
  • Writing clear documentation

Certifications and licenses worth listing

  • AWS Certified Data Engineer, Associate: a solid signal for AWS shops.
  • Google Cloud Professional Data Engineer: the equivalent for GCP and BigQuery roles, and well regarded.
  • Databricks Certified Data Engineer Associate or Professional: worth listing for lakehouse roles.
  • SnowPro Core: useful where Snowflake is the warehouse; consultancies often ask for it.
  • dbt Analytics Engineering Certification: for analytics engineering roles.

Length and format

One page up to about eight years of experience. Keep certifications near the bottom but visible; they help with recruiters, especially at consultancies and cloud partners. Two pages only once you have led platform work across several teams.

Common mistakes

  • Tool soup: a bullet listing six technologies and no outcome. Pick the one or two that mattered and say what changed.
  • No volumes. “Built pipelines” could mean a CSV a day or a billion events.
  • Leaving out data quality. Hiring managers have been burned by engineers who move data fast and wrong.
  • Describing only greenfield builds. Migrations, cost cuts and reliability fixes are often the most impressive work.
  • Calling every SQL transformation “ETL development” in a modern ELT shop. Mirror the job ad’s vocabulary.

Questions and answers

Should a data engineer list programming languages separately from tools?

Yes, group them: languages, orchestration and modeling, storage and compute, cloud and infrastructure. It reads faster and helps applicant tracking systems match keywords.

Is a data engineer resume closer to software engineering or analytics?

Closer to software engineering, with analytics customers. Show engineering habits (tests, CI, code review) and name the teams who rely on your data.

Do I need a cloud certification?

It is not required, but it helps more in data engineering than in analytics, especially at consultancies and companies that partner with a cloud vendor.

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