Data Engineer Resume Example (2026 Guide)
Data Engineer resume example
A complete sample in the Modern template FitMyRole exports. The person and employers are fictional; the structure, skills and bullet style are what to copy.
Derek Langford
[email protected] • (555) 017-3365 • Chicago, IL • linkedin.com/in/dereklangford
Professional Summary
Senior Data Engineer with 12 years of experience building batch and streaming pipelines, data warehouses and the platforms analysts and data scientists rely on. Leads the data platform at a freight company that moves 2 billion events a month from telematics, transportation management and billing systems into Snowflake through Kafka, Spark and dbt, with a 99.9% on-time SLA for the 7 a.m. operations reports. Cut warehouse spend by 38% while data volume doubled, and moved 400 legacy SSIS and stored-procedure jobs to Airflow and dbt with tests on every model. Sets standards for data modeling, testing and on-call for a team of five engineers.
Technical Skills
- Languages: SQL, Python, PySpark, Scala, T-SQL, Bash
- Pipelines & Orchestration: ETL and ELT, Apache Airflow, dbt, Spark Structured Streaming, Apache Kafka, Kafka Connect, Debezium (CDC), SSIS
- Data Warehousing & Modeling: Snowflake, Amazon Redshift, Databricks, Delta Lake, dimensional modeling (Kimball), slowly changing dimensions, semantic layer
- Cloud & Infrastructure: AWS (S3, EMR, Glue, Lambda, IAM), Terraform, Docker, GitHub Actions, PostgreSQL, Oracle, SQL Server
- Data Quality & Observability: dbt tests, Great Expectations, Monte Carlo, data contracts, freshness SLAs, data lineage
- Leadership: Technical design reviews, on-call and incident response, mentoring, hiring, stakeholder requirements
Education & Certification
- Coursework focus: Database Systems, Data Structures, Distributed Systems and Statistics.
Work Experience
Senior Data EngineerFebruary 2021 – PresentIronbridge Freight, Chicago, IL
- Cut data freshness for dispatch dashboards from 24 hours to 5 minutes by building a pipeline that streams 2 billion telematics events a month from Kafka into Snowflake with Spark Structured Streaming on Databricks.
- Migrated 400 SSIS packages and stored procedures to dbt models orchestrated by Airflow over 14 months, adding 1,900 dbt tests and retiring the on-premises SQL Server warehouse on schedule.
- Reduced Snowflake spend by 38% in a year while data volume doubled by right-sizing warehouses, clustering the 6 largest fact tables and converting full refreshes to incremental dbt models.
- Hold a 99.9% on-time SLA for the 7 a.m. operations and billing reports with freshness checks, Monte Carlo observability alerts and a runbook-based on-call rotation.
- Designed the Kimball star schemas for shipments, loads and invoices that 60 analysts query, with a documented semantic layer in dbt that replaced 3 conflicting revenue definitions.
- Built change data capture from 3 PostgreSQL and Oracle source systems with Debezium and Kafka Connect, replacing nightly full extracts that took 4 hours.
- Lead a team of 5 data engineers through design reviews, pull request standards and Terraform-managed AWS infrastructure, and hired 3 of them.
Data EngineerJune 2017 – January 2021Midway Health Partners, Chicago, IL
- Built Python and Airflow pipelines that load claims, eligibility and EHR extracts from 40 payer and hospital partners into Amazon Redshift, processing 300 million claim lines a year under HIPAA controls.
- Shortened the nightly batch from 7 hours to 2.5 by rewriting the claims ETL in PySpark on Amazon EMR and partitioning Parquet files in S3 by service month.
- Created a data quality framework with Great Expectations that checks 250 rules on every partner file, rejecting bad loads before reporting and cutting data incident tickets by 60%.
- Delivered the member and provider data marts behind 25 Tableau dashboards for care management and finance, working from requirements gathered with each team.
- Masked protected health information in non-production environments with a tokenization job, and the platform passed the annual HIPAA security audit with no findings.
ETL DeveloperJuly 2014 – May 2017Lakeview Mutual Insurance, Chicago, IL
- Developed and maintained 120 SSIS packages and T-SQL stored procedures loading policy, claims and agent data into the SQL Server data warehouse.
- Brought month-end close reporting from 9 hours to 3 by rewriting row-by-row cursor logic as set-based SQL and indexing the 10 slowest queries.
- Built Type 2 slowly changing dimensions for policy and agent tables, giving actuaries accurate point-in-time history for reserving.
- Automated the daily agent commission feed with SQL Server Agent jobs and failure alerts, removing a 2-hour manual process for the finance team.
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Tailor my resumeWhy This Resume Works
- The summary gives scale before skills: 2 billion events a month, a 99.9% on-time SLA, 400 legacy jobs migrated and a team of five, which is the scope a senior data engineering hiring manager checks first.
- Bullets carry the numbers data engineers are judged on: freshness (24 hours to 5 minutes), runtime (7 hours to 2.5), cost (38% lower Snowflake spend) and reliability (99.9% SLA).
- The stack appears inside the work, not just in a list: Kafka, Spark Structured Streaming, dbt, Airflow, Debezium and Great Expectations each show up in a bullet that says what they did.
- Data modeling is visible ("Kimball star schemas for shipments, loads and invoices that 60 analysts query"), which separates a warehouse builder from someone who only moves files.
- Twelve years read as a logical climb from SSIS and T-SQL to cloud pipelines to platform lead, so the legacy migration in the current role is backed by real experience with the old stack.
Common Resume Mistakes
- Listing only tools without scale or outcome.
- Missing ETL, warehouse, or orchestration keywords.
- No volume or SLA metrics.
Keywords Recruiters Look For
- Data engineering
- ETL
- Spark
- SQL
- Pipeline
- dbt
- Airflow
- Data warehouse
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