Scribd
Principal Analytics Engineer, AI Native Self Service Analytics
San Francisco · Principal · Contract
Sponsorship not specifiedDetected 6 days ago
PythonSQLDatabricksAWSData AnalysisData EngineeringData ScienceData VisualizationAgentic AIIncident ResponseResearchCommunicationCollaboration
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odds of building a lasting career here
40Risky
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
Thin sponsorship signal and lottery-bound. A low-probability bet with your clock running. Prioritize cap-exempt roles and proven entry-level sponsors first.
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Files H-1B transfers
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About the role
- 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.
Responsibilities
- 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.
- We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas.
- Come join us in building something meaningful.
- 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
- Demonstrated ability to lead cross functional data initiatives from strategy through production adoption and measurable business impact
Requirements
- 10+ years of experience in analytics engineering, data engineering, business intelligence engineering or a related discipline
- Advanced SQL skills and production experience with modern data stack, including AWS, Databricks, Airbyte, Fivetran, Looker, or comparable platforms
Compensation
- At Scribd, Inc., your base pay is one part of your total compensation package and is determined within a range.
Benefits
- Comprehensive health, dental, and vision coverage
- Mental health support and disability coverage
- Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals
- Paid parental leave and family support benefits
- Retirement matching and employee equity
- Learning and development programs and professional growth opportunities
- Wellness and home office stipends
- Strong judgment in balancing immediate stakeholder needs with long term investments in reusable data products, platform health, and technical debt reduction
- Scribd Flex (flexible work model)
Company info
- 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.
This listing is sourced directly from Scribd's careers page and normalized into a canonical job model.