Senior Data Engineer Resume Sample

Derek Williams

derek@williams.dev

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(415) 555-0410

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linkedin.com/in/derek-williams-data

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github.com/derekwilliams

Senior Data Engineer

Senior Data Engineer with 9+ years architecting large-scale data platforms. Led data infrastructure serving 100+ data consumers, processing 500M+ daily events, reduced infrastructure costs by 40%, and improved data freshness by 75%. Expert in Spark, Kafka, data architecture, and technical strategy. Mentor engineering teams and drive data platform decisions org-wide.

WORK EXPERIENCE

Senior Data Engineer

Mar 2020 – Present

FinTech Unicorn (Pre-IPO)

Data Platform Architecture: Architected lakehouse data platform processing 500M+ daily events serving 100+ data scientists and analysts, achieving 99.95% uptime and sub-15min data freshness
Cost & Performance Optimization: Reduced data infrastructure costs by 40% ($1.2M annually) through optimized Spark jobs, auto-scaling, and migrating from EMR to Databricks, improved query performance by 75%
Technical Leadership & Strategy: Mentored 8 data engineers, drove data architecture decisions, established data quality standards and governance framework adopted by 40-person data org

Data Engineer → Senior Data Engineer

Jun 2016 – Feb 2020

E-Commerce Platform (Public)

Real-Time Data Streaming: Built real-time streaming platform with Kafka and Flink processing 200M+ events/day, enabling real-time analytics and ML features with <5min latency
Data Lake & Warehouse Migration: Led migration from on-prem data warehouse to AWS data lake (S3 + Athena + Glue), migrated 50TB+ data and 200+ ETL jobs with zero downtime
Data Modeling & Governance: Designed dimensional models for 10+ business domains, implemented data catalog and lineage tracking, established dbt best practices adopted by 15-person team

SKILLS & COMPETENCIES

CERTIFICATIONS

Databricks Certified Data Engineer Professional

May 2022

Databricks

EDUCATION

Master of Science in Computer Science

2014-2016

University of California, Berkeley

Berkeley, California

Database Systems
Distributed Computing

Tools to build your Senior Data Engineer resume

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

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Resume Headlines

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

Senior Data Engineer | Data Platform Architecture | 500M+ Daily Events

Senior Data Engineer | Spark, Kafka, Databricks | $1.2M Cost Savings

Staff Data Engineer | Technical Leadership | Mentoring 8+ Engineers

Senior Data Engineer | Lakehouse Architecture | 99.95% Uptime at Scale

Senior Data Engineer | Data Strategy | Real-Time & Batch Processing

Senior Data Engineer | Big Data Expert | Serving 100+ Data Consumers

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

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Power Bullet Points

Adapt these achievement-focused bullets to showcase your impact.

Data Platform Architecture & Strategy

• Architected lakehouse data platform on Databricks processing 500M+ daily events serving 100+ data scientists/analysts with 99.95% uptime and sub-15min data freshness

• Designed data mesh architecture distributing data ownership across 8 domain teams, implementing federated governance and self-serve data platform

• Built unified data platform supporting batch and streaming workloads, ML feature engineering, and analytics reducing time-to-insight from days to hours

• Drove data architecture decisions for 40-person data org, evaluating technologies, establishing patterns, and defining 3-year data platform roadmap

Cost & Performance Optimization

• Reduced data infrastructure costs by 40% ($1.2M annually) through Spark optimization, auto-scaling, and migrating from AWS EMR to Databricks

• Improved query performance by 75% through intelligent caching, materialized views, Z-ordering, and optimized Spark configurations

• Scaled platform from 50M to 500M daily events (10x growth) while reducing costs through efficient resource utilization and spot instances

• Implemented FinOps practices with cost monitoring, chargebacks, and optimization recommendations preventing $500K+ wasteful spend

Real-Time Streaming & Data Lake

• Built real-time streaming platform with Kafka and Apache Flink processing 200M+ events/day enabling real-time analytics and ML with <5min latency

• Led migration from on-prem data warehouse to AWS data lake (S3, Athena, Glue) migrating 50TB+ data and 200+ ETL jobs with zero downtime

• Implemented lambda architecture combining batch (Spark) and streaming (Flink) layers providing both real-time and historical analytics

• Designed data retention and archival strategies managing petabytes of data with tiered storage (hot/warm/cold) optimizing costs

Technical Leadership & Mentorship

• Mentored 8 data engineers on Spark optimization, streaming architectures, and data modeling, advancing 3 from mid to senior level through structured development

• Established data quality standards, dbt best practices, and governance framework adopted across 40-person data org improving data reliability

• Led data engineering guild of 15 engineers driving knowledge sharing, establishing patterns, and promoting engineering excellence

• Drove adoption of modern data stack (Databricks, dbt, Fivetran) training 20+ engineers and improving development velocity by 50%

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

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Resume Writing Tips for Senior Data Engineers

Lead with Platform-Level Data Architecture

Senior data engineers build platforms, not pipelines. Lead with: event scale (500M+ daily), consumers enabled (100+), platform architecture (lakehouse, data mesh), org-wide impact. Show your work enables entire data orgs.

Demonstrate Data Technical Strategy

Include strategic leadership: drove architecture decisions, established data patterns, chose technologies (Databricks vs EMR), defined roadmaps. Show you define the future of data engineering—not just execute, you decide how data works.

Showcase Data Systems Expertise

Senior data means deep systems knowledge. Include: lakehouse architecture, data mesh, streaming (Kafka/Flink), governance, lineage, observability. Show expertise beyond ETL—you understand data platforms at a systems level.

Quantify Team Building and Organizational Impact

Senior engineers multiply through people. Quantify: engineers mentored (8+), guild leadership (15 engineers), standards established, org adoption ($1.2M saved). Show you build data teams and culture, not just platforms.

Position for Staff/Principal Data Track

List 15-20 skills spanning big data (Spark expert), streaming (Kafka/Flink), cloud (AWS expert), orchestration, governance, leadership, strategy. Show you're not just executing—you're defining data engineering at your company.

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Essential Skills & Keywords

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

Data Platform Architecture

Data Platform Architecture Lakehouse Architecture Data Mesh Data Strategy Platform Engineering System Design

Big Data & Batch (Expert)

Apache Spark (Expert) PySpark & Spark SQL Scala Spark Optimization Databricks (Expert) AWS EMR

Streaming & Real-Time

Apache Kafka Apache Flink Spark Structured Streaming Real-Time Pipelines Stream Processing Event-Driven Architecture

Cloud & Infrastructure (Expert)

AWS (Expert: S3, EMR, Glue, Redshift, Athena) Databricks Snowflake Data Lakes Infrastructure as Code Cost Optimization (FinOps)

Data Modeling & Transformation

dbt (Expert) Data Modeling (Expert) Dimensional Modeling Data Warehousing Kimball Methodology Data Vault

Data Governance & Quality

Data Governance Data Lineage Data Catalogs Data Quality Frameworks Compliance (GDPR, CCPA) Observability

Orchestration & Automation

Apache Airflow (Expert) Workflow Orchestration CI/CD for Data Automation Monitoring & Alerting

Leadership & Strategy

Technical Leadership Mentorship Data Strategy Architecture Decision Making Team Building Cross-Functional Collaboration

💡 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 strategy and leadership: Architected platform for 100+ consumers, 500M events—senior scope requires platform thinking org-wide
Technical strategy and decision-making: Drove data architecture, technology choices, migration strategies—senior engineers shape long-term direction
Scale and cost optimization: 40% cost reduction, 500M events, 75% freshness improvement—demonstrates efficiency at massive scale
Organizational leadership: Mentored 8 data engineers, established standards, led guild—senior engineers multiply impact through teams

✓ ATS-Friendly Elements

Senior-level keywords: "data platform architecture," "technical leadership," "big data," "streaming," "data strategy"
Strategic verbs: Architected, Led, Drove, Established, Mentored, Scaled
Business impact: cost savings, data freshness, platform reliability, team enablement
Technical depth: real-time streaming, data lake, lakehouse, data mesh, observability
9+ years experience with clear progression to senior/staff level

✓ Human-Readable Design

Summary positions as data technical leader: platform architecture, team mentorship, strategic decisions
Metrics reflect senior scope: 500M events, 100+ consumers, 40% cost savings, org-wide impact
Experience shows progression: Engineer → Senior → Staff/Principal level
Demonstrates influence: shaped architecture, drove strategy, mentored teams
Balance deep technical work with leadership, strategy, and organizational impact

💡 Key Takeaways

Senior data engineers architect platforms and drive data strategy, not just build pipelines
Quantify impact at scale: events processed, cost savings, data consumers enabled
Show technical leadership: drove architecture, established patterns, mentored data teams
Demonstrate data systems thinking: lakehouse, data mesh, streaming, governance, lineage
Balance hands-on data work with strategy, mentorship, and organizational influence

📈 Career Progression in Data Engineering

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

💾 Junior Engineer → ⚙️ Data Engineer → 🚀 Senior/Staff Engineer (Current)

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