SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 191,500 - 268,000 / annual
Scribd, Inc. is seeking a Director of Analytics Engineering to lead and scale the analytics engineering discipline across the organization as it transforms into an AI-native company. You will build, scale, and lead a high-performing Analytics Engineering team, transforming strong individual contributors into a cohesive and highly effective unit.
The Analytics Engineering team is central to Scribd's data strategy. The team transforms fragmented data, inconsistent definitions, and dispersed analytical knowledge into trusted, reusable, production-quality data foundations and products that enable intelligence to scale across the company. In partnership with Engineering, Analytics Engineers ensure critical data is captured, centralized, documented, secured, and governed. The team builds canonical datasets, shared metrics, and semantic layers, together with the quality, observability, lineage, and evaluation systems that make these assets trustworthy. Analytics Engineers also develop AI agents and analytical tools that bring reliable intelligence directly into the decisions and workflows of teams across the company.
As Director, your leadership philosophy is grounded in service: providing clarity, creating context, removing obstacles, and helping others succeed. You will strengthen team dynamics, ensure great work is recognized, create meaningful career pathways, and help individuals navigate obstacles and opportunities. You will establish an operating model that creates structure without unnecessary bureaucracy, fostering culture and community that balances local priorities with broader company success.
Your pragmatic approach keeps the organization moving forward. Rather than waiting for an ideal end state, you 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. 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.
You approach AI and agentic technologies with curiosity and pragmatism, applying them where they create clear and measurable value. 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.
Scribd operates under a Scribd Flex model, empowering employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture. Occasional in-person attendance is required for all employees.
REQUIREMENTS:
- 10+ years of experience in analytics engineering, data engineering, business intelligence engineering, or a related discipline
- 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 valued as strategic partners
- Depth in building governed, canonical data models and semantic layers, giving core business concepts one authoritative, discoverable definition
- Experience designing 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
NICE TO HAVES:
- Advanced Python
- Experience leading data cleanup efforts and implementing role-based access controls and data retention policies
- 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
