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# Lead Analytics Engineer, AI Native Self Service Analytics

- **Company:** Scribd Inc.
- **Location:** Austin, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $149,000.0 - $253,500.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Information Engineering, Data Retention, Data Systems, Data Warehousing, Python (Programming Language), Sql Optimization, Reliability of Systems, Technical Debt, Agentic-AI, Data Layers, Looker Analytics, Databricks
- **Published:** October 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=9dae16cfefd3ab07

## About the Role

* 10/8+ 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

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 leading on call rotations, coordinating incident response, resolving production issues, and driving follow up improvements that strengthen system reliability

## Description

We're hiring for a Principal/Lead Analytics Engineer 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.