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Salary: USD 179,000 - 253,500 / annual
Scribd is hiring a Principal/Lead Analytics Engineer to scale data foundations for an AI-native organization. You will shape technical standards, modeling practices, and development approaches that enable Analytics Engineering to move faster with confidence.
As a senior individual contributor, you'll provide technical leadership without direct people management. Your role bridges strategy and execution: you'll connect Analytics Engineering investments to company priorities, communicate tradeoffs to senior leaders, and guide decisions on data modeling, architecture, governance, quality, tooling, and developer experience.
You'll work across Scribd's four product portfolio (Scribd, Slideshare, Everand, Fable) to transform fragmented data and inconsistent definitions into trusted, reusable, production-quality data foundations. In partnership with Engineering, you'll ensure critical data is captured, centralized, documented, secured, and governed. You'll build canonical datasets, shared metrics, and semantic layers alongside quality, observability, lineage, and evaluation systems that make these assets trustworthy.
Your pragmatic approach prioritizes moving forward: rather than waiting for an ideal end state, you think in sequences and ask "What is the right next version?" You'll develop AI agents and analytical tools that bring reliable intelligence directly into decisions and workflows across the company. You approach AI and agentic technologies with curiosity and pragmatism, applying them where they create clear, measurable value.
Scribd operates on a Flex model with occasional in-person attendance required. The company values GRIT: setting and achieving Goals, delivering Results, contributing Innovative ideas, and strengthening the Team through collaboration.
REQUIREMENTS:
- 10+ years of experience in analytics engineering, data engineering, business intelligence engineering, or related discipline
- Depth in building governed, canonical data models and semantic layers that give 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 at scale
- Experience designing and building canonical data models and semantic layers supporting 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 (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 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 leading on-call rotations, coordinating incident response, resolving production issues, and driving follow-up improvements
