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Director of Analytics Engineering

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Salary

California: $202,500 - $268,000 / United States (outside of California): $191,500 - $254,000

Location

San Francisco, CA

Posted

Today

We’re hiring for a Director, Analytics Engineering to own scaling data for an AI native Scribd, Inc. You will build, scale, and lead a high performing Analytics Engineering team, turning strong individual contributors into a cohesive and highly effective team. This requires more than hiring and developing exceptional talent. It means establishing an operating model that creates structure without unnecessary bureaucracy, fostering culture and community that balances local priorities with the broader success of the company, and creating the technical foundations and standards of excellence that enable everyone to do their best work.

Your leadership philosophy is grounded in service. A leader’s role is to provide clarity, create context, remove obstacles, and help others succeed. You will strengthen team dynamics, ensure great work is recognized, create meaningful career pathways, and help individuals navigate obstacles and opportunities throughout their careers. Durable teams are built by distributing ownership, developing leaders, and creating an environment where people feel trusted, supported, and accountable.

Your pragmatic approach keeps the organization moving forward. Rather than waiting for an ideal end state, you will think in sequences and ask, “What is the right next version?” Communication happens early and often because transparency, thoughtful feedback, and visible decision making build trust and help teams move with confidence.

Your strong strategic judgment is paired with enough proximity to the work to guide technical decisions credibly. You will connect analytics engineering investments to company priorities, communicate tradeoffs clearly to senior leaders, and contribute thoughtfully to decisions about data modeling, architecture, governance, quality, tooling, and developer experience.

The transformation happening through AI is an opportunity to expand what talented teams can accomplish. Agentic coding, AI assisted development and analysis, and automated workflows can increase human judgment, creativity, and impact rather than replace them. You approach these technologies with curiosity and pragmatism, applying them where they create clear and measurable value.

Above all, leadership is about building people and strengthening the discipline they share. Success is reflected in a team that grows more capable, confident, connected, and effective because of your leadership.

The essentials

  • 10+ years of experience in analytics engineering, data engineering, business intelligence engineering or a related discipline, including 5+ years leading and developing technical teams
  • Proven track record of building, scaling, and leading high performing teams, turning strong individual contributors into cohesive and highly effective teams that are valued as strategic partners
  • Depth in building governed, canonical data models and semantic layers, giving core business concepts one authoritative, discoverable definition instead of letting teams reconcile competing sources
  • Experience designing the data quality, observability, documentation, governance, access control, privacy, lineage, and evaluation systems that ensure data and AI products are reliable, explainable, trustworthy, and safe to operate at scale
  • Advanced SQL skills and production experience with modern data stack, including AWS, Databricks, Airbyte, Fivetran, Looker, or comparable platforms
  • Demonstrated ability to lead cross functional data initiatives from strategy through production adoption and measurable business impact
  • Strong judgment in balancing immediate stakeholder needs with long term investments in reusable data products, platform health, and technical debt reduction
  • Excellent written and verbal communication with technical teams, business stakeholders, and senior executives

The nice to haves

  • Advanced Python
  • Experience leading data cleanup efforts and implementing role based access controls and data retention policies to improve data quality, security, and governance
  • Experience designing and building canonical data models and semantic layers that support financial analysis at scale
  • Experience leading on call rotations, coordinating incident response, resolving production issues, and driving follow up improvements that strengthen system reliability

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