Data & Analytics

Mid LevelData Engineer Resume Example

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Data Engineer Resume Template (mid level)

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Professional Summary

Data engineer with 5 years of experience building reliable data pipelines and scalable warehouse models that support analytics, reporting, and machine learning teams.

Professional Experience

Data EngineerMar 2022 - Present
Harbor Analytics, Boston, MA
  • Built batch and near-real-time pipelines processing 2TB of data daily using Apache Airflow and Spark.
  • Improved pipeline reliability to 99.8% by implementing DataDog alerting and automated retries.
  • Redesigned the core data warehouse schema in Snowflake, reducing BI query times by 37%.
Data Analyst / Junior Data EngineerJul 2019 - Feb 2022
Beacon Insights, Boston, MA
  • Transitioned manual Excel reporting into automated SQL transformations using dbt.
  • Partnered with engineering to migrate legacy reporting data into a cloud data warehouse.
  • Created automated data quality checks to detect null values and schema drift.

Skills

Python, SQL, ETL and ELT Pipelines, Data Warehousing (Snowflake), Airflow, Spark, Data Modeling, Docker

Education

B.S. in Data ScienceMay 2019
Northeastern University

Certifications

  • Databricks Certified Data Engineer Associate

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Key Skills for Data Engineer

PythonSQLETL and ELT PipelinesData Warehousing (Snowflake)AirflowSparkData ModelingDocker

ATS Keywords for Data Engineer

Use these naturally in your summary, skills, and experience when they reflect your background.

Data EngineerPythonSQLETL and ELT PipelinesData WarehousingAirflowSparkData ModelingDockerDatabricks Data Engineer Associatesoftware developmenttroubleshooting

How to Write a Mid Level Data Engineer Resume

At the mid-level, a Data Engineer resume must show ownership, not observation. Prove you've independently designed and shipped ETL or ELT pipelines feeding a warehouse like Snowflake, orchestrated with Airflow, and that analytics, reporting, or ML teams depend on the data you deliver daily.

Resume Writing Tips

Quantify Pipeline Reliability, Not Just Existence

State uptime or SLA numbers for pipelines you own — '99.5% on-time delivery across 40 Airflow DAGs' beats 'built ETL pipelines.' Mid-level hiring managers want evidence you keep production data flowing without hand-holding.

Highlight Data Modeling and Warehouse Ownership

Describe a Snowflake or warehouse schema you designed or refactored — star schema, slowly changing dimensions, partitioning — and connect it to a business outcome like faster dashboard load times or lower query cost.

Show Spark and Docker in Production Context

Note a Spark job you optimized for runtime or cost, and mention containerizing pipelines with Docker for consistent deployment — these details prove you operate beyond notebooks, in systems other teams rely on.

Skills to Highlight

Airflow

Name the number of DAGs you maintain and how you handle failures — retries, alerting, backfills — to show you own orchestration reliability, not just scheduling scripts.

Data Warehousing (Snowflake)

Describe a specific warehouse redesign or cost optimization, like clustering keys or query pruning, that reduced compute spend or improved dashboard performance for downstream analysts.

Spark

Reference a Spark job's data volume and the tuning you did — partitioning, caching, broadcast joins — to demonstrate you can handle scale beyond pandas dataframes.

ETL and ELT Pipelines

Distinguish pipelines you designed from scratch versus ones you inherited and improved, and quantify impact in latency reduced, data volume handled, or downstream teams served.

Common Questions

How is a mid-level Data Engineer resume different from an entry-level one?

It replaces coursework and side projects with owned production systems. Instead of describing what you learned, describe what you shipped and maintained independently — pipeline uptime, warehouse design decisions, and the analytics or ML teams whose work depends on your data.

Should I mention Databricks Data Engineer Associate certification even with 5 years of experience?

Yes, but keep it secondary to your project bullets. At this level, certifications support your credibility rather than carry it — recruiters weigh demonstrated pipeline ownership and measurable reliability far more heavily than a credential alone.

What metrics matter most for a mid-level Data Engineer resume?

Pipeline uptime or SLA adherence, data volume processed, latency reduced, and cost savings from warehouse or Spark tuning. Pair each metric with the business team it served — analytics, reporting, or ML — to prove real cross-functional impact.

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