Director, Data Engineering (AI Native)
New
Within the US and CanadaFull-TimeDirector
SalaryUSD 216,000 to 318,000 USD (US); 251,000 to 295,000 CAD (Canada)
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Job Details
- Experience
- 8–10+ years of experience in data engineering, analytics engineering, or data platform roles, with at least 5 years in people management and 3+ years managing managers.
- Required Skills
- People ManagementStrategyData engineeringStakeholder managementData modelingdbtDatabricks
Requirements
- 8–10+ years of experience in data engineering, analytics engineering, or data platform roles at technology companies.
- 5+ years in people management, including 3+ years managing managers.
- Deep technical credibility across data platform architecture and analytics engineering.
- Strong expertise with dbt (data build tool), including project structure, testing, and CI/CD.
- Production experience with Databricks or equivalent lakehouse platforms (Snowflake, BigQuery) at consumer scale.
- Proven experience managing multiple teams or workstreams simultaneously (15+ people across distinct functions).
- Strong track record of stakeholder management at the director/VP level.
- Demonstrated ability to translate complex technical constraints into business-aligned strategy.
- Strong business acumen connecting data infrastructure to product metrics and monetization.
- Active AI-native mindset with a point of view on how AI transforms data engineering productivity.
Responsibilities
- Define and drive the technical roadmap across data platform, analytics engineering, and ads data infrastructure.
- Own the analytics engineering strategy including dbt project structure, data modeling standards, and testing practices.
- Oversee the data platform, covering Databricks infrastructure, pipeline orchestration, and reliability/observability.
- Lead and develop three engineering managers and their teams, managing workforce planning and hiring strategy.
- Serve as the primary point of contact for data engineering across Product, Engineering, Finance, and Marketing.
- Manage budget and cost control for data engineering infrastructure and vendor spend.
- Drive an AI-native approach by leveraging AI tools to accelerate development and optimize pipelines.
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