Senior Data Engineer Resume Example That Passes ATS Screening
This Senior Data Engineer resume example demonstrates the keywords and formatting that pass ATS screening. Key terms to include: senior data engineer, data pipeline, Spark, Airflow.
Senior data engineer resumes must demonstrate pipeline architecture ownership, data platform scalability, and cross-team enablement — not just ETL scripting. ATS systems for senior data roles screen for distributed data systems, cloud data warehouse experience, and data governance keywords. This example shows how a senior data engineer positions for Staff or Principal-track roles.
Full Resume Sample
Olga Petrov
Senior Data Engineer
Professional Summary
Senior data engineer with 7 years of experience building and scaling data platforms processing 5TB+ daily across batch and real-time pipelines. Designed the data lakehouse architecture at Spotify that powers analytics for 200+ data consumers. Expert in Spark, Airflow, dbt, and Snowflake with a focus on data quality, governance, and self-service enablement.
Experience
Senior Data Engineer — Data Platform
Spotify · New York, NY · Apr 2021 - Present
- Designed and implemented a data lakehouse architecture on GCP (BigQuery + GCS) processing 5TB+ daily, serving 200+ analysts, data scientists, and product managers across 15 teams
- Built a self-service data pipeline framework using Airflow and dbt that reduced new pipeline creation time from 2 weeks to 2 days, enabling 40+ non-engineering teams to build their own data products
- Led the migration of 300+ legacy Hive jobs to Spark on Dataproc, reducing compute costs by 45% and improving job completion times by 60%
- Implemented a data quality monitoring system using Great Expectations integrated with Airflow, catching 95% of data issues before they reached production dashboards
- Mentored 3 junior data engineers and authored internal documentation establishing data engineering best practices adopted across the data platform organization
Data Engineer → Senior Data Engineer
Capital One · McLean, VA · Jun 2018 - Mar 2021
- Built real-time fraud detection data pipelines using Apache Kafka and Spark Streaming, processing 1M+ transactions/hour with sub-second latency requirements
- Designed and maintained a feature store serving machine learning models with 500+ features for credit risk and fraud detection, supporting 50+ data science model deployments
- Created an automated data lineage tracking system that mapped dependencies across 800+ datasets, enabling impact analysis for regulatory compliance
- Promoted from Data Engineer to Senior Data Engineer based on platform design contributions and cross-team technical leadership
Data Engineer
Booz Allen Hamilton · Washington, DC · Aug 2017 - May 2018
- Developed ETL pipelines using Python and Apache NiFi for a federal healthcare analytics platform, ingesting data from 12 source systems
- Built automated data validation checks that reduced data quality incidents by 70% across the ingestion layer
Education
M.S. Information Systems Management — Carnegie Mellon University, 2017
Skills
Data Processing: Apache Spark, Apache Kafka, Apache Airflow, dbt, Apache NiFi, Beam
Cloud & Storage: GCP (BigQuery, Dataproc, GCS, Pub/Sub), AWS (Redshift, Glue, S3, EMR), Snowflake, Delta Lake
Languages: Python, SQL, Scala, Java, Bash
Data Quality & Governance: Great Expectations, Data lineage, Schema evolution, Data contracts, Metadata management
Certifications
Google Cloud Professional Data Engineer (2022) · Databricks Certified Data Engineer Associate (2023)
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Compare Your Resume →Why This Resume Works
Architecture ownership is the headline, not just pipeline building. The summary leads with 'designed the data lakehouse architecture' — not 'built ETL pipelines.' Senior data engineer ATS screening prioritizes architecture, platform design, and scalability keywords over individual pipeline work.
Data volume and consumer count provide dual scale metrics. 5TB+ daily processing AND 200+ data consumers. The first shows technical scale; the second shows organizational impact. Both are critical for senior data engineering positions where platform enablement matters as much as technical execution.
Self-service enablement demonstrates platform thinking. Reducing pipeline creation from 2 weeks to 2 days and enabling 40+ non-engineering teams shows Olga thinks about data as a product, not just a technical system. This is the exact mindset senior/staff data engineering roles demand.
Data quality and governance keywords are prominent. Great Expectations, data lineage, data contracts, metadata management — these governance-focused keywords are increasingly screened for senior data roles as organizations mature their data platforms.
ATS Keywords for Senior Data Engineer Resumes
ATS systems scanning Senior 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
Name the architecture you designed, the data volume, and the consumer count. Senior data engineer summaries should show you build platforms, not just pipelines.
Experience Section
Lead with architecture and platform design bullets. Include data volume (TB/day), processing rates (events/hour), and consumer counts (teams served). Show the business impact of data infrastructure: cost savings, time reduction, quality improvement.
Skills Section
Include a 'Data Quality & Governance' category alongside technical tools. This signals platform maturity thinking and is increasingly screened for senior data roles.
Education Section
For 7+ years of experience, education is minimal. Cloud and data engineering certifications (GCP, Databricks) carry more weight for ATS matching.
Common Senior Data Engineer Resume Mistakes
Hiring managers reviewing Senior Data Engineer resumes flag these problems repeatedly. Each one can knock your ATS score or land your application in the rejection pile.
- Describing pipeline work without mentioning data volume, processing rates, or downstream consumer count
- Omitting data quality and governance keywords — these distinguish senior from mid-level data engineers
- Listing Spark, Airflow, and dbt without explaining what you designed or architectured with them
- Not showing platform enablement impact (how many teams or users did your data platform serve?)
- Missing cloud-specific certifications that provide strong ATS keyword matches for data engineering roles