Principal Analytics Engineer, Scribd

• 10+ years of experience in analytics engineering, data engineering, business intelligence engineering or a related discipline
• 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
• Experience designing and building canonical data models and semantic layers that support self-serve analytics at scale for Finance, Legal, Operations, Marketing, Research, and other business teams
• 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

Desirable

• Data & Analytics is building the DNA of an AI native Scribd, Inc. Bringing together Analytics Engineering, Data Science, and Data Analytics, D&A partners across product, technology, content, and the business to scale intelligence, advance understanding, shape strategy, and turn insight into lasting growth across our four brand portfolio
• Making this ambition real at scale starts with Analytics Engineering. 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
• On top of this foundation, Analytics Engineers develop AI agents and analytical tools that bring reliable intelligence directly into the decisions and workflows of teams across the company
• We’re hiring Principal & Lead Analytics Engineers to scale data for an AI native Scribd, Inc. In this role, you will shape the technical foundations, modeling standards, and development practices that allow Analytics Engineering to move faster with greater confidence. You will operate as a senior individual contributor, deeply connected to the work, trusted for your judgment, and able to raise the quality of systems, decisions, and execution across teams
• Your impact will come through technical leadership. 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
• 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
• 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, this role is about strengthening the discipline of Analytics Engineering. Success is reflected in data systems that are more reliable, understandable, scalable, and useful, and in teams that become more capable because of your technical leadership