Director of Data Engineering
Directors of Data Engineering manage data platforms and pipelines. They own data infrastructure, quality, and accessibility.
Median Salary
$280,000
Job Growth
High — data infrastructure increasingly critical
Experience Level
Entry to Leadership
Salary Progression
| Experience Level | Annual Salary |
|---|---|
| Entry Level | $180,000 |
| Mid-Level (5-8 years) | $280,000 |
| Senior (8-12 years) | $380,000 |
| Leadership / Principal | $520,000+ |
What Does a Director of Data Engineering Do?
Directors of Data Engineering lead data platform and engineering teams. They design and build scalable data infrastructure, establish data governance and quality standards, own data accessibility and democratization, manage data engineers building pipelines, and partner with analytics and ML teams.
A Typical Day
Strategy: Define data platform strategy and roadmap
Architecture: Design data architecture for growing scale
Team: Manage data engineers, career development, hiring
Quality: Establish data quality standards and monitoring
Infrastructure: Oversee infrastructure (data warehouse, pipelines, lakes)
Governance: Ensure data security, privacy, compliance
Collaboration: Work with analytics and ML teams on requirements
Key Skills
Career Progression
Directors of Data Engineering typically progress to VP of Engineering or Chief Data Officer roles.
How to Get Started
Data engineering: 8+ years as data engineer
Systems design: Deep experience designing data systems
Leadership: Team management and organizational skills
Big data: Experience with large-scale data platforms
Communication: Strong technical and business communication
Company: Work at data-intensive companies (tech, finance, retail)
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Frequently Asked Questions
What's the scope?▼
Data platform, pipelines, warehouse, data quality, governance, accessibility for analytics and ML.
How big are teams?▼
Typically 10-30 data engineers reporting to director. Large companies may have multiple directors.
What's the impact?▼
Data infrastructure enables all analytics and ML. Bad infrastructure kills efficiency and innovation. Critical role.
What's the biggest challenge?▼
Scaling (petabyte-scale data), data quality (garbage in, garbage out), governance (data security and compliance), adoption.
What skills matter most?▼
Data architecture, systems design, big data tech stack, team leadership, business understanding.
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Last updated: 2026-03-07