Data Engineer Resume Example
A data engineer resume is evaluated on pipeline ownership measured by system scalability, not tool lists without volume context.
This resume is for data engineers who own end-to-end ETL processes and architectural decisions, but aren't yet responsible for org-wide data strategy or department budget management.
- Ownership of end-to-end data pipeline architecture and lifecycle
- Evidence of measurable impact on system performance or operational costs
- Proficiency in modern data stack technologies and infrastructure orchestration
- Professional summary highlighting core technical competencies and ownership
- Experience section utilizing nested bullet points for specific project outcomes
- Technical skills categorized by functional application and toolsets
Andres Cruz
Summary
Experience
- Architected a real-time data ingestion pipeline using Kafka and Spark Streaming to process 250M daily events into Snowflake with sub-minute latency.
- Optimized complex dbt transformation models, reducing warehouse compute expenditures by $82K annually through query refactoring and incremental loading strategies.
- Spearheaded the migration of 12 critical Airflow DAGs to a containerized Kubernetes executor, increasing pipeline uptime from 98.2% to 99.9%.
- Managed the data architecture for a merchant analytics dashboard, supporting 15,000 monthly active internal users across product and finance teams.
- Developed automated ETL workflows using Python and Airflow to aggregate data from 6 external SaaS APIs into a centralized Redshift data warehouse.
- Refined SQL aggregation logic for executive reporting suites, cutting dashboard load times by 38% for the global operations department.
- Mentored 2 data engineering interns on SQL optimization techniques and dimensional data modeling principles.
- Established data quality monitoring using Great Expectations, identifying and resolving 8 critical schema drift issues before downstream reporting impact.
Education
Skills
Python · SQL · Airflow · Spark · AWS · Data Modeling · Snowflake · dbt · Kafka · Redshift · Docker · Terraform · Git · ETL
What makes this resume effective
- This resume meets the hiring bar for data engineers by demonstrating architectural ownership, significant cost savings, and high-uptime system management.
- Andres's work at Robinhood shows deep technical ownership by detailing the specific use of Kafka and Spark Streaming to handle 250M daily events.
- The entry for Asana highlights measurable efficiency gains, such as the 38% reduction in dashboard load times achieved through SQL optimization.
How to write better bullet points
Managed Airflow DAGs for data pipelines.
Spearheaded the migration of 12 critical Airflow DAGs to a containerized Kubernetes executor, increasing pipeline uptime from 98.2% to 99.9%.
It replaces a vague task with a specific migration project and a clear reliability metric.
Used dbt to transform data in Snowflake.
Optimized complex dbt transformation models, reducing warehouse compute expenditures by $82K annually through query refactoring.
It moves from simple tool usage to demonstrating significant financial impact on the business.
Built an ingestion pipeline for event data.
Architected a real-time data ingestion pipeline using Kafka and Spark Streaming to process 250M daily events with sub-minute latency.
It specifies the architecture, the massive scale of data, and the performance outcome.
Data Engineer resume writing tips
- Connect pipeline builds to specific business outcomes like cost reduction or reduced latency.
- Detail the specific volume of data handled to prove system scalability.
- Mention specific tools used for monitoring and quality, such as Great Expectations.
Common mistakes
- Listing tools without context; hiring managers need to see how you applied Python or Spark to solve a specific bottleneck.
- Focusing only on maintenance; failing to show where you improved a system or migrated to a better architecture suggests stagnation.
- Omitting data scale; a pipeline for 1,000 rows is vastly different from one for 100 million, so always specify volume.
Frequently asked questions
Is this resume right for someone with 4-7 years of experience? Yes if you own the entire data lifecycle and make architectural choices; no if your experience is limited to executing predefined tickets.
Yes if you own the entire data lifecycle and make architectural choices; no if your experience is limited to executing predefined tickets.
Yes, if you have moved beyond simple script writing to owning entire data lifecycles. No, if your work is still strictly defined by tickets and you do not make architectural choices.
What if my background is primarily in on-premise environments instead of cloud? Yes, because the core logic of pipeline reliability and complexity remains the same regardless of whether you use cloud-native or on-prem tools.
Yes, because the core logic of pipeline reliability and complexity remains the same regardless of whether you use cloud-native or on-prem tools.
You can still use this structure by replacing cloud-native tools like Snowflake or AWS with your specific stack. The focus remains on the complexity and reliability of the pipelines you built.
What if I don't know the exact dollar amount I saved the company? Focus on technical performance indicators like load time reductions or system uptime to prove the before-and-after state of your pipelines.
Focus on technical performance indicators like load time reductions or system uptime to prove the before-and-after state of your pipelines.
You can substitute financial metrics with technical performance indicators, such as the 38% reduction in load times shown in the Asana experience. Focus on the before and after state of the system.
How much should I change before applying? Keep the impact-heavy structure but swap specific technologies like BigQuery or Redshift to align with the specific job description requirements.
Keep the impact-heavy structure but swap specific technologies like BigQuery or Redshift to align with the specific job description requirements.
Keep the structure of the impact-heavy bullets but swap the specific technologies to match the job description. For example, if a role requires BigQuery instead of Redshift, ensure your transformation examples reflect that shift.
What do hiring managers focus on for professionals in this role? Evidence of high-uptime systems and an understanding of the trade-offs between technical performance and long-term operational costs.
Evidence of high-uptime systems and an understanding of the trade-offs between technical performance and long-term operational costs.
They look for evidence that you can build systems that do not break and that you understand the cost-to-performance trade-offs of your technical decisions.
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