Data Engineer Resume Example
Data engineering resumes should make freshness, reliability, scale, and cost visible across the pipeline. This example connects Spark, dbt, Kafka, Snowflake, Airflow, Delta Lake, and data contracts to processing and self-service outcomes.
Adapt it by tracing data from ingestion through transformation and consumption, naming where quality checks, lineage, partitioning, or orchestration protected the flow.
Data Engineer Resume Sample
Saurabh Ghosh
Data Engineer
Kolkata, West Bengal · saurabh.ghosh@email.com · +91 97654 34567 · linkedin.com/in/saurabhghosh · github.com/saurabhg
Professional Summary
Data Engineer with 3.5 years building scalable data pipelines and lakehouses for e-commerce and media analytics teams. Reduced data processing cost by 40% by migrating Spark workloads to Databricks Delta Lake. Proficient in Python, PySpark, Apache Airflow, dbt, and Snowflake. Passionate about data reliability, pipeline observability, and enabling self-serve analytics.
Data Engineer Technical Skills
Languages: Python, SQL, Scala (basics)
Batch / Streaming: Apache Spark (PySpark), Apache Kafka, Apache Flink, Databricks
Orchestration: Apache Airflow, Prefect, dbt (Core + Cloud)
Warehouses: Snowflake, BigQuery, Redshift, Delta Lake
Storage: AWS S3, GCS, Azure ADLS, Parquet, Delta, Iceberg
Data Quality: Great Expectations, dbt tests, Soda Core, Monte Carlo
DevOps: Docker, Terraform, GitHub Actions, dbt CI
Practices: ELT over ETL, lakehouse architecture, schema evolution, data contracts, SLA monitoring
Professional Experience
- Migrated 45 legacy Hadoop MapReduce jobs to PySpark + Databricks Delta Lake; reduced processing cost by 40% and job runtime from 6 hours to 45 minutes.
- Designed dbt-based transformation layer with 200+ models, tests, and documentation; enabled 10 analysts to own their own data transformations without engineering support.
- Built a real-time inventory availability Kafka pipeline ingesting 50K events/minute from 8 warehouse systems into Snowflake with <30s latency.
- Implemented data contract validation using Great Expectations on all ingestion entry points; reduced pipeline-breaking schema issues from 3/month to zero.
- Set up data lineage tracking in OpenLineage, giving analysts full upstream dependency visibility for debugging bad numbers.
- Built Airflow-orchestrated ELT pipelines ingesting data from 15 social media and ad-platform APIs into BigQuery, powering daily reach and engagement dashboards.
- Developed incremental Spark jobs for content recommendation feature engineering, processing 200M events/day.
- Introduced Parquet + partitioning strategy on S3 data lake, reducing query cost on Athena by 55%.
Data Engineer Projects
Open-source CDC (Change Data Capture) framework from PostgreSQL/MySQL to Delta Lake tables. 400+ GitHub stars.
Local Airflow + dbt development environment with pre-configured connections. 600+ downloads.
Education
B.Tech Computer Science — Jadavpur University, 2020 · CGPA 8.5 / 10
Certifications
- Databricks Certified Data Engineer Associate
- dbt Analytics Engineering Certification
- Google Professional Data Engineer
Key Achievements
- DeltaSync — 400+ GitHub stars, featured in Data Engineering Weekly #89
- Jadavpur University — Department Rank 1 (2020)
All details in this resume example are illustrative and should be replaced with your actual experience, achievements, education, and certifications.
Practical guidance for writing, structuring, and customizing a strong Data Engineer resume.
How to Write a Data Engineer Resume
Start with pipeline shape: batch migration, streaming ingestion, ELT transformation, or feature preparation. State source, destination, orchestration, and consumer when the source data supports them.
Use Spark job counts, events per minute, model counts, runtime, latency, or daily volume to establish scale. Pair each with the exact Databricks, Kafka, dbt, or storage action.
Reliability deserves its own evidence: data contracts, Great Expectations, dbt tests, OpenLineage, SLA monitoring, schema evolution, and CI explain how trustworthy data reached analysts.
Built scalable ETL pipelines and maintained data warehouses.
Migrated 45 legacy Hadoop MapReduce jobs to PySpark + Databricks Delta Lake; reduced processing cost by 40% and job runtime from 6 hours to 45 minutes.
What to Include in a Data Engineer Resume
Include Python/SQL, Spark and streaming, Airflow or orchestration, dbt transformations, Snowflake or warehouse work, lakehouse/storage formats, data quality, lineage, IaC, and measurable cost, runtime, freshness, or reliability. Add a certifications subsection because this source includes Databricks Certified Data Engineer Associate; dbt Analytics Engineering Certification; Google Professional Data Engineer; on your resume, list only credentials you actually hold and preserve their official names.
Data Engineer Resume Summary Example
Summarize the pipeline and platform scale you own, then add one verified processing-cost, runtime, latency, or reliability improvement.
Data Engineer with 3.5 years building scalable data pipelines and lakehouses for e-commerce and media analytics teams. Reduced data processing cost by 40% by migrating Spark workloads to Databricks Delta Lake. Proficient in Python, PySpark, Apache Airflow, dbt, and Snowflake. Passionate about data reliability, pipeline observability, and enabling self-serve analytics.
Important Data Engineer Skills for a Resume
Languages
Python, SQL, Scala (basics)
Batch / Streaming
Apache Spark (PySpark), Apache Kafka, Apache Flink, Databricks
Orchestration
Apache Airflow, Prefect, dbt (Core + Cloud)
Warehouses
Snowflake, BigQuery, Redshift, Delta Lake
Storage
AWS S3, GCS, Azure ADLS, Parquet, Delta, Iceberg
Data Quality
Great Expectations, dbt tests, Soda Core, Monte Carlo
DevOps
Docker, Terraform, GitHub Actions, dbt CI
Practices
ELT over ETL, lakehouse architecture, schema evolution, data contracts, SLA monitoring
Only include skills you can defend with a project, production example, or troubleshooting story.
Data Engineer Resume Experience Examples
Senior Data Engineer
Migrated 45 legacy Hadoop MapReduce jobs to PySpark + Databricks Delta Lake; reduced processing cost by 40% and job runtime from 6 hours to 45 minutes.
Senior Data Engineer
Built a real-time inventory availability Kafka pipeline ingesting 50K events/minute from 8 warehouse systems into Snowflake with <30s latency.
Senior Data Engineer
Designed dbt-based transformation layer with 200+ models, tests, and documentation; enabled 10 analysts to own their own data transformations without engineering support.
Data Engineer
Introduced Parquet + partitioning strategy on S3 data lake, reducing query cost on Athena by 55%.
Use real numbers when you can verify them. Do not invent metrics simply to make the resume sound stronger.
Data Engineer ATS Keywords
Choose keywords that match both the Data Engineer job description and work you can substantiate. Spell out important concepts naturally in summary and experience instead of pasting this list.
Data Engineer Resume Tips
Trace source to consumer
Name ingestion source, processing layer, destination, and analyst or product use.
Quantify data movement
Use actual jobs, models, events per minute, records per day, runtime, or freshness.
Show dbt ownership
Connect models, tests, documentation, and analyst self-service.
Make contracts operational
Explain where validation ran and which schema failures it prevented.
Expose storage design
Tie Parquet, partitioning, Delta, or Iceberg decisions to query or processing behavior.
Frequently Asked Questions
What should a Data Engineer resume include?
Include ingestion and transformation architecture, Python/SQL, Spark, Kafka, Airflow, dbt, warehouses, storage formats, data quality, lineage, CI, and measured pipeline outcomes.
What skills should I put on a Data Engineer resume?
Use source-backed terms such as PySpark, Apache Kafka, Databricks, Airflow, dbt, Snowflake, Delta Lake, Parquet, Great Expectations, OpenLineage, Docker, and Terraform.
How do I write a strong Data Engineer resume summary?
State the batch or streaming platform scope and add one verified cost, runtime, event-latency, analyst-enablement, or reliability result.
What experience should I highlight on a Data Engineer resume?
Highlight Hadoop-to-Spark migration, dbt layers, streaming inventory, data contracts, lineage, API ingestion, feature jobs, and lake partitioning.
What ATS keywords matter for a Data Engineer resume?
ATS keywords commonly include data engineer, ETL, ELT, Spark, PySpark, Kafka, Airflow, dbt, Snowflake, Databricks, Delta Lake, data contracts, and data quality.
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