Data Engineer Resume Example

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Data Engineer Resume Sample

Samantha Lee
samantha@lee.dev
•
(646) 555-0395
•
linkedin.com/in/samantha-lee-data
•
github.com/samanthalee
Data Engineer
Data Engineer with 5 years architecting and building scalable data infrastructure. Led development of data platform processing 50M+ events daily, reduced data latency by 60%, and improved pipeline reliability to 99.9%. Expert in Python, Spark, AWS, and data architecture. Strong in technical leadership, data governance, and cross-functional collaboration.
WORK EXPERIENCE
Data Engineer
May 2022 – Present
AdTech Unicorn
  • Data Platform Architecture: Architected real-time data platform processing 50M+ daily events using Kafka, Spark, and AWS, reducing data latency from 30min to 12min (60% improvement)
  • Pipeline Reliability & Monitoring: Improved pipeline reliability from 97% to 99.9% by implementing automated testing, monitoring, alerting, and self-healing mechanisms
  • Data Governance & Quality: Established data quality framework with automated validation checks, implemented data lineage tracking with dbt, reduced data incidents by 70%
Data Engineer
Aug 2019 – Apr 2022
Media Streaming Platform
  • ETL & Data Warehouse: Built 30+ Airflow pipelines ingesting data from 15 sources into Snowflake data warehouse, serving 50+ analysts and data scientists
  • Spark & Big Data Processing: Developed PySpark jobs processing 5TB+ daily data, optimized Spark jobs reducing runtime from 4hrs to 1.5hrs (62% faster)
  • Data Modeling & Analytics: Designed dimensional models and dbt transformations for user behavior analytics, implemented incremental models improving freshness from 12hrs to 2hrs
SKILLS & COMPETENCIES
Python (PySpark, pandas) | Apache Spark (Expert) | Apache Kafka & Streaming | SQL (Advanced) | AWS (S3, EMR, Glue, Redshift, Lambda) | Apache Airflow | Snowflake & Databricks | dbt (Data Build Tool) | Data Modeling | Data Warehousing | Data Quality & Governance | Scala (Intermediate)
CERTIFICATIONS
Databricks Certified Data Engineer Associate
Oct 2023
Databricks
EDUCATION
Bachelor of Science in Data Science
2015-2019
New York University
New York, New York
  • Data Engineering
  • Distributed Systems

Tools to build your Data Engineer resume

Copy and adapt these proven examples to create a resume that stands out.

✨

Resume Headlines

Use these attention-grabbing headlines to make a strong first impression.

Data Engineer | Spark, Kafka, AWS | Processing 50M+ Daily Events
Mid-Level Data Engineer | Data Platform Architecture | 60% Latency Reduction
Data Engineer | Real-Time & Batch Processing | 99.9% Pipeline Reliability
Data Engineer | Modern Data Stack | Python, Spark, Kafka, Snowflake
Mid-Level Data Engineer | Data Quality Focus | 70% Incident Reduction
Data Engineer | Scalable Data Pipelines | Serving 50+ Analysts

💡 Tip: Choose a headline that reflects your unique value proposition and matches the job requirements.

⚡

Power Bullet Points

Adapt these achievement-focused bullets to showcase your impact.

Data Platform Architecture

• Architected real-time data platform processing 50M+ daily events using Kafka, Spark Streaming, and AWS reducing data latency from 30min to 12min (60% improvement)
• Designed lakehouse architecture on Databricks unifying batch and streaming workloads, reducing infrastructure costs by 35% while improving query performance
• Built scalable ETL framework processing 5TB+ daily data across 30+ pipelines serving 50+ analysts and data scientists
• Implemented medallion architecture (bronze/silver/gold layers) improving data quality by 45% and enabling incremental processing

Streaming & Real-Time Data

• Built real-time streaming pipelines using Apache Kafka and Spark Structured Streaming processing 50M+ daily events with sub-minute latency
• Developed Kafka consumer applications with exactly-once semantics and idempotency handling 10K+ messages/sec with zero data loss
• Implemented Change Data Capture (CDC) using Debezium and Kafka Connect streaming database changes in real-time for 8 downstream systems
• Created event-driven data pipelines with Kafka topics, schema registry (Avro), and stream processing reducing data freshness from 30min to 2min

Batch Processing & Optimization

• Developed 30+ PySpark jobs processing 5TB+ daily data, optimizing Spark jobs reducing runtime from 4hrs to 1.5hrs (62% faster) through partitioning and caching
• Implemented incremental data processing with dbt reducing pipeline runtime by 75% and enabling hourly refreshes instead of daily batch jobs
• Optimized Spark cluster configuration (executor memory, cores, partitions) reducing compute costs by 40% while maintaining SLAs
• Built dynamic partition pruning and predicate pushdown in Spark queries improving performance by 50% on large datasets

Data Quality & Governance

• Established data quality framework with automated validation checks, Great Expectations rules, and dbt tests improving data reliability from 97% to 99.9%
• Implemented data lineage tracking with dbt docs and data catalog enabling 50+ users to understand data dependencies and transformations
• Reduced data incidents by 70% through proactive monitoring, alerting, and automated data quality checks catching issues before business impact
• Created data governance policies for PII handling, data retention, and access controls ensuring compliance with GDPR and data regulations

💡 Tip: Replace generic terms with specific metrics, technologies, and outcomes from your experience.

📝

Resume Writing Tips for Data Engineers

1

Emphasize Platform-Level Thinking

Mid-level data engineers own platforms, not just pipelines. Highlight: architected data platform, designed lakehouse, established frameworks. Show you think about data architecture holistically—how the system works, not just individual ETL jobs.

2

Quantify Scale and Business Impact

Connect technical work to business outcomes. Include: events processed (50M+ daily), latency improvements (60% faster), pipeline reliability (99.9%), users served (50+ analysts). Show data engineering drives business value.

3

Demonstrate Batch and Streaming Expertise

Mid-level means depth in both. Show: batch processing (Spark, Airflow, dbt), streaming (Kafka, Spark Streaming, CDC). Include optimizations, performance tuning, and architectural decisions. Balance shows maturity.

4

Show Data Quality and Governance Leadership

Include governance beyond code: data quality frameworks, lineage tracking, monitoring, governance policies. Mid-level engineers establish practices that teams follow—not just execute, you define standards.

5

List 12-15 Skills Across the Data Stack

Cover Python (PySpark), Spark (expert), streaming (Kafka), cloud (AWS expert), orchestration (Airflow), transformation (dbt), governance. Show T-shaped: deep data engineering expertise with broad competence in architecture and quality.

🎯

Essential Skills & Keywords

Include these skills to optimize your resume for ATS systems and recruiter searches.

Programming & Data Processing

Python (Expert) PySpark pandas SQL (Advanced) Scala (Intermediate) Data Processing

Big Data & Batch Processing

Apache Spark (Expert) Spark SQL Spark Optimization Databricks AWS EMR Batch ETL

Streaming & Real-Time

Apache Kafka Spark Structured Streaming Kafka Connect Change Data Capture (CDC) Real-Time Pipelines Event-Driven Architecture

Cloud & Infrastructure

AWS (S3, Glue, EMR, Redshift, Lambda) Snowflake Databricks Cloud Storage Serverless Infrastructure as Code

Orchestration & Workflow

Apache Airflow (Advanced) DAG Optimization Workflow Design Scheduling Monitoring & Alerting

Data Modeling & Transformation

dbt (Expert) Data Modeling Dimensional Modeling Star Schema Incremental Models Medallion Architecture

Data Quality & Governance

Great Expectations Data Quality Frameworks Data Lineage Data Catalogs Data Governance Compliance (GDPR)

Best Practices

Technical Leadership Performance Optimization Cost Optimization Documentation Code Reviews Mentorship

💡 Tip: Naturally integrate 8-12 of these keywords throughout your resume, especially in your summary and experience sections.

Why this resume works

Role-Specific Strengths

  • Data platform architecture: Built platform processing 50M events daily—shows architectural ownership beyond individual pipelines
  • Performance and reliability engineering: 60% latency reduction, 99.9% reliability—demonstrates production-grade data engineering at scale
  • Modern data stack expertise: Spark, Kafka, Airflow, AWS, Snowflake, dbt—versatility across batch, streaming, and cloud data tools
  • Data governance and quality leadership: Established data quality framework, implemented governance—mid-level engineers drive standards

✓ ATS-Friendly Elements

  • Mid-level keywords: "data architecture," "data platform," "Spark," "Kafka," "streaming," "data warehouse"
  • Action verbs: Architected, Led, Designed, Optimized, Implemented
  • Business outcomes: data latency, pipeline reliability, data quality, analyst productivity
  • Technologies: Python, Spark, Kafka, AWS, Airflow, Snowflake, dbt
  • Demonstrates progression from junior to mid with increasing architectural ownership

✓ Human-Readable Design

  • Summary balances technical depth with business impact
  • Metrics show broader scope: 50M events, 60% latency reduction, 99.9% reliability
  • Experience demonstrates ownership: architected platform, made technology choices
  • Shows both execution and influence: built pipelines AND improved infrastructure
  • Technology choices show maturity: streaming (Kafka), batch (Spark), orchestration (Airflow)

💡 Key Takeaways

  • Mid-level data engineers own data platform architecture, not just individual pipelines
  • Quantify scale and impact: events processed, latency improvements, pipeline reliability
  • Show architectural thinking: data modeling, streaming vs batch, data governance
  • Demonstrate technology breadth: batch (Spark), streaming (Kafka), orchestration, cloud
  • Balance technical execution with data quality, governance, and stakeholder collaboration

📈 Career Progression in Data Engineering

See how Data Engineering roles evolve from pipeline development to platform architecture.

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