Data Engineer Resume Example That Passes ATS Screening
Data engineering resumes often fall into the trap of reading like a list of tools rather than a record of problems solved. Hiring managers want to see that you can build reliable pipelines, handle messy data at scale, and work cross-functionally with analysts and data scientists. This example uses a mistakes-lead layout to highlight what most candidates get wrong before showing what a strong mid-level data engineer resume looks like.
Common Data Engineer Resume Mistakes
Hiring managers reviewing Data Engineer resumes flag these problems repeatedly. Each one can knock your ATS score or land your application in the rejection pile.
- Listing every tool you have ever touched without indicating proficiency level or context of use.
- Writing bullets that describe pipeline architecture without mentioning the data volume, source count, or consumer impact.
- Omitting data quality and governance work, which is increasingly what separates mid-level data engineers from junior ones.
- Failing to mention collaboration with analysts or data scientists, which makes you look like you build pipelines in isolation.
- Describing migrations or refactors without quantifying the improvement, leaving the reader to guess whether the effort was worthwhile.
- Using the same resume format as a software engineer without adapting it to highlight data-specific concerns like freshness, lineage, and reliability.
Full Resume Sample
Yusuf Bazargan
Data Engineer
Professional Summary
Data engineer with 5 years of experience designing and maintaining batch and real-time data pipelines in cloud-native environments. Currently responsible for a lakehouse platform on Databricks and AWS that ingests 2.3TB of raw data daily from 45+ source systems, serving a team of 20 analysts and data scientists at a mid-size e-commerce company. Focused on pipeline reliability, data quality enforcement, and reducing time-to-insight for business stakeholders. Previously built ETL infrastructure at a healthcare analytics startup where I was the sole data engineer supporting a 200M-row clinical dataset.
Experience
Data Engineer II
Nomad Commerce · Denver, CO · Aug 2022 - Present
- Own the end-to-end data platform built on Databricks, Delta Lake, and AWS (S3, Glue, Redshift Spectrum), ingesting 2.3TB daily from 45+ sources including Shopify, Salesforce, payment processors, and clickstream events
- Designed and implemented a medallion architecture (bronze/silver/gold) that standardized data quality expectations across the organization, reducing downstream data incident tickets from 35 per month to fewer than 5
- Built a real-time streaming pipeline using Kafka and Spark Structured Streaming to deliver sub-minute inventory and order data to the operations team, replacing a batch process that ran on a 4-hour lag
- Created a self-service data catalog using DataHub, tagging 800+ datasets with ownership, freshness SLAs, and lineage metadata, which cut analyst onboarding time for new data sources from 2 weeks to 3 days
- Introduced dbt for transformation layer management, migrating 120+ legacy SQL scripts into version-controlled, tested models with 94% test coverage across critical business metrics
Data Engineer
Veridian Health Analytics · Boulder, CO · Jun 2020 - Jul 2022
- Served as the sole data engineer at a 40-person healthcare analytics startup, building and maintaining the ETL infrastructure that powered clinical outcomes reporting for 15 hospital system clients
- Designed an ingestion framework using Apache Airflow and Python that normalized HL7 and FHIR clinical data from disparate EHR systems into a unified analytical schema on Snowflake
- Reduced pipeline failure rate from 18% to under 2% by implementing comprehensive data validation checks, automated alerting via PagerDuty, and a dead-letter queue pattern for malformed records
- Built row-level security and HIPAA-compliant data access controls in Snowflake, enabling 15 client organizations to query their own data without risk of cross-tenant exposure
Education
Bachelor of Science in Computer Science — University of Colorado Boulder, 2020 (Minor in Applied Mathematics. Senior capstone project on distributed stream processing.)
Skills
Data Platforms & Storage: Databricks / Delta Lake, Snowflake, AWS (S3, Glue, Redshift Spectrum, Lambda), PostgreSQL, Apache Iceberg
Pipeline & Orchestration: Apache Airflow, dbt, Spark (PySpark, Structured Streaming), Apache Kafka, Fivetran, Great Expectations
Programming & Query Languages: Python, SQL, Scala, Bash scripting, Terraform (IaC)
Data Quality & Governance: DataHub (data catalog), Great Expectations, Medallion architecture, HIPAA compliance, Data lineage and SLA tracking
Certifications
Databricks Certified Data Engineer Associate · AWS Certified Data Analytics - Specialty
See how your resume scores against ATS systems
Check Your ATS Score Free →Why This Resume Works
Pipeline reliability improvements are quantified with before-and-after metrics that hiring managers can evaluate immediately. Going from 35 data incident tickets per month to fewer than 5 is a concrete outcome that any hiring manager understands. Similarly, reducing pipeline failure rate from 18% to 2% tells a clear story of engineering discipline. These numbers matter because data engineering is ultimately about trust. If stakeholders cannot rely on the data, nothing else matters. Yusuf's resume proves reliability through measurement, not just assertion.
The sole-engineer startup role demonstrates breadth and ownership that larger-team roles often obscure. Being the only data engineer at a 40-person startup means Yusuf made architectural decisions, handled on-call, managed vendor relationships, and shipped features without handing work off to specialists. This is a powerful signal for mid-level hiring because it shows the candidate can operate independently. Many data engineers at large companies only touch one piece of the stack. The startup role proves end-to-end capability.
The dbt migration bullet shows modern tooling adoption driven by a real business need. Migrating 120+ legacy SQL scripts to dbt with 94% test coverage is not just a tooling upgrade. It is a story about bringing engineering rigor to a transformation layer that was previously ungoverned. Hiring managers reading this see someone who identifies a maintainability problem and solves it with the right tool, not someone who adopts dbt because it is trendy. The test coverage number adds credibility.
ATS Keywords for Data Engineer Resumes
ATS systems scanning Data Engineer applications look for these terms. The resume above weaves them in naturally rather than listing them outright.
Section-by-Section Writing Tips
Professional Summary
State the volume of data you handle, the number of source systems, and who consumes the output. These three details let a hiring manager calibrate your experience level in seconds. Mention your primary cloud platform and one or two areas of focus like reliability or real-time processing to signal specialization.
Experience Section
Every bullet should connect a technical action to a business or operational outcome. 'Built a Kafka pipeline' is incomplete. 'Built a Kafka pipeline that replaced a 4-hour batch lag with sub-minute delivery for the operations team' tells the reader why it mattered. Use volume metrics (TB ingested, sources integrated, models maintained) to establish scale.
Skills Section
Group tools by function, not by popularity. A recruiter needs to see that you cover storage, orchestration, transformation, and governance. Listing 15 tools in a flat list forces them to do the categorization themselves, and they will not bother.
Education Section
For mid-level data engineers, a CS or related degree is worth including but should not dominate the resume. If you have relevant certifications from Databricks, AWS, or GCP, list them in a separate section since they carry real weight in data engineering hiring.