Data Engineer resume example

• Entry level

  Data engineer with a computer science degree and a year of internship experience writing Python and SQL pipelines. Built a daily Airflow job that loaded 2 million rows into BigQuery and added dbt tests that caught bad records before dashboards broke. Looking to grow on a team that values clean models and dependable data.

• Mid level

  Data engineer with five years of experience building batch and streaming pipelines on AWS. Moved ingestion from nightly cron scripts to Airflow and Kafka, bringing report freshness from a day to fifteen minutes. Comfortable with Spark, Snowflake and dbt, and known for documenting models so analysts can self-serve.

• Senior

  Senior data engineer with nine years designing data platforms for analytics and machine learning teams. Led a lakehouse migration that cut warehouse cost by a third while holding reliability above 99.9 percent. Sets modeling standards, reviews architecture across squads and mentors engineers moving from analytics into platform work.

• Listing tools without the scale they ran at

  Fix: Spark and Airflow on a resume mean nothing until a reader knows the volume. Add rows, events or terabytes per day, plus the number of jobs or tables you owned, so a hiring manager can place your experience on the right size of system.

• Calling every SQL script a pipeline

  Fix: A pipeline is scheduled, monitored, retryable and safe to re-run. If yours was, say so with words like idempotent, backfill, SLA or alerting. If it was an ad hoc query, call it analysis and keep the pipeline label for real production jobs.

• Leaving out data quality and reliability

  Fix: Teams hire data engineers to be trusted, not just fast. Add a bullet on freshness SLAs, schema checks, failed-run rates or incidents you prevented, using the tests you wrote in dbt, Great Expectations or plain SQL.

• Never naming the consumer of the data

  Fix: Close the loop on each major bullet. Say whether the output fed finance reporting, a recommendation model or a customer-facing dashboard, and what changed for that team once the data arrived on time.

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• Built a Kafka and Spark streaming job handling about 1.2 billion events per day, cutting dashboard freshness from four hours to under ten minutes.
• Replaced 60 cron-driven shell scripts with Airflow DAGs that retry and alert, dropping missed morning loads from roughly twice a week to once a quarter.
• Redesigned a Snowflake clustering and warehouse sizing plan that reduced monthly compute spend by 35 percent without slowing the finance close.
• Wrote dbt tests and freshness checks for 120 models, catching broken upstream feeds before analysts saw them and cutting data incident tickets by half.
• Migrated a 40 TB Hadoop cluster to S3 and Parquet with partition pruning, making common queries about six times faster and retiring the on-premise hardware.
• Created a change data capture feed from Postgres to the warehouse, giving product analysts near real-time order data for the first time.
• Standardized a star-schema layer that eight analytics teams reused, trimming new metric delivery from two weeks to three days.
• Added column-level PII masking and access policies across 90 tables, helping the company pass a SOC 2 audit with no data findings.
• Cut a nightly backfill from nine hours to ninety minutes by switching from row-by-row inserts to partitioned bulk loads.
• Documented lineage for the revenue pipeline in a data catalog, so on-call engineers traced failures in minutes instead of reading code.