Junior Data Engineer Resume Example
A junior data engineer resume stands out by demonstrating measurable pipeline efficiency instead of listing tools without production context.
This resume is for junior data engineers who own automated ETL pipelines and data quality checks, but aren't yet responsible for architecting entire data platforms or setting org-wide data governance.
- Proficiency in building and maintaining automated ETL/ELT pipelines
- Evidence of optimizing data processing for cost or speed
- Capability to implement data quality and validation frameworks
- Professional summary highlighting specific cloud and orchestration tools
- Experience section utilizing bullet points for technical achievements
- Projects section showcasing end-to-end data ingestion and modeling
Stephanie Richardson
Summary
Experience
- Engineered 6 automated ETL pipelines using Python and Airflow, migrating legacy on-premise data to Snowflake and reducing batch processing time by 22%.
- Optimized complex SQL transformations in dbt, which decreased monthly cloud compute costs by $12K while maintaining data integrity for the merchant analytics dashboard.
- Implemented data quality validation frameworks using Great Expectations, identifying and resolving 15+ upstream schema drifts before they impacted downstream reporting.
- Spearheaded the containerization of data processing scripts using Docker, ensuring environment parity across development and production stages for a team of 12 engineers.
Education
Skills
Python · SQL · Airflow · Spark · AWS · Data Modeling · dbt · Snowflake · Docker · Git · PostgreSQL · Bash · ETL · Data Pipelines
Projects
Crypto Stream Processor
Built a real-time ingestion engine using Python and Kafka to track price volatility across 5 exchanges, storing results in a PostgreSQL database for trend analysis.
Python, Kafka, PostgreSQL, Docker
Warehouse Schema Design
Designed a star-schema data warehouse for a mock e-commerce platform, implementing dbt models to transform raw JSON events into clean analytical tables in BigQuery.
SQL, dbt, BigQuery, Git
What makes this resume effective
- This resume meets the hiring bar for junior data engineers by demonstrating proficiency in pipeline automation, cloud cost optimization, and data quality implementation.
- Notice how Stephanie's work at Capital One specifically highlights reducing batch processing time by 22% using Airflow, proving she can deliver measurable efficiency gains.
- See how the inclusion of 'Great Expectations' for resolving schema drifts signals a proactive approach to data reliability that distinguishes early-career professionals.
How to write better bullet points
Wrote Python scripts to move data to the warehouse.
Engineered 6 automated ETL pipelines using Python and Airflow, migrating legacy on-premise data to Snowflake.
It specifies the tools, the scale of the task, and the specific destination, providing much-needed technical context.
Fixed bugs in SQL queries to save money.
Optimized complex SQL transformations in dbt, decreasing monthly cloud compute costs by $12K while maintaining data integrity.
It demonstrates mastery of dbt and provides a high-impact financial metric that proves the value of the optimization.
Used Docker for my data projects.
Spearheaded the containerization of data processing scripts using Docker, ensuring environment parity across development and production stages.
It explains the 'why' behind using the tool—standardizing environments—rather than just listing the technology used.
Junior Data Engineer resume writing tips
- Link your ETL tasks to specific business outcomes like cost reduction or improved data freshness.
- Detail the specific libraries and frameworks used to solve data quality issues in your pipelines.
- Quantify how your optimizations impacted cloud compute expenses or processing latency.
Common mistakes
- Listing tools without context: Many juniors list 'Spark' or 'Kafka' but don't explain how they applied them to solve a specific data bottleneck.
- Focusing only on 'learning': Hiring managers need to see what you actually shipped to production, not just what you studied in a course.
- Ignoring data quality: Failing to mention validation or testing suggests you might produce 'dirty' data that breaks downstream dashboards.
Frequently asked questions
Is this resume right for someone with only internship experience? Yes, if you owned specific pipelines from end-to-end; avoid purely academic projects that lack exposure to production-grade tools and scale.
Yes, if you owned specific pipelines from end-to-end; avoid purely academic projects that lack exposure to production-grade tools and scale.
Yes, if you have owned specific features or pipelines from start to finish during those internships. If your experience is limited to purely academic projects without exposure to production-grade tools like Airflow or Snowflake, you should focus on the projects section more heavily.
What if I used different cloud providers like GCP or Azure instead of AWS? Yes, because core principles are cloud-agnostic; replace AWS services with their GCP or Azure equivalents while maintaining the same structure.
Yes, because core principles are cloud-agnostic; replace AWS services with their GCP or Azure equivalents while maintaining the same structure.
The core principles of data engineering remain the same across providers, so you should keep the structure and simply swap the specific technologies. In this resume, Stephanie uses AWS, but replacing it with BigQuery or Azure Data Factory would serve the same hiring signal.
What if I don't know the exact dollar amount I saved the company? Use volume metrics or percentages, such as reduced processing time or event handling capacity, to provide a measurable signal of your impact.
Use volume metrics or percentages, such as reduced processing time or event handling capacity, to provide a measurable signal of your impact.
You can use percentages or volume metrics instead, such as reduced processing time or increased daily event handling. In this resume, Stephanie uses both dollar amounts and percentages to provide a well-rounded view of her impact.
How much should I change the projects section if I have more work experience? Prioritize professional roles by trimming projects, but retain those that show high-level technical skills not fully captured in your job history.
Prioritize professional roles by trimming projects, but retain those that show high-level technical skills not fully captured in your job history.
If you have more than a year of full-time experience, you should trim the projects to make room for more detailed bullets under your professional roles. However, keep projects that demonstrate high-demand skills like Kafka or real-time streaming if they aren't part of your current daily responsibilities.
What do hiring managers focus on at this level? Managers look for evidence that you can reliably move clean, cost-effective data using tools like dbt for transformations and Airflow for orchestration.
Managers look for evidence that you can reliably move clean, cost-effective data using tools like dbt for transformations and Airflow for orchestration.
Recruiters look for evidence that you can handle the 'plumbing' of data—moving it reliably while ensuring it is clean and cost-effective. Stephanie signals this by highlighting her use of dbt for transformations and Great Expectations for validation.
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