Lead Analytics Engineer, AI Native Self Service Analytics

Scribd•San Francisco, CA

About The Position

Scribd, Inc. is hiring a Principal/Lead Analytics Engineer to shape the technical foundations, modeling standards, and development practices that enable Analytics Engineering to scale trusted data and self-service analytics across the company. The role is a senior individual-contributor position with hands-on involvement and technical leadership across teams. It connects Analytics Engineering investments to company priorities, communicates tradeoffs to senior leaders, and helps guide decisions on data modeling, architecture, governance, quality, tooling, and developer experience. The successful candidate will strengthen the discipline of Analytics Engineering by building reliable, understandable, scalable, and useful data systems, and by increasing team capability through technical leadership. The position calls for pragmatic, transparent decision-making; balancing immediate stakeholder needs with reusable data products and long-term platform health; and applying AI-assisted development, analysis, agents, and automated workflows where they provide clear, measurable value. The Data & Analytics team brings together Analytics Engineering, Data Science, and Data Analytics to partner across product, technology, content, and business. Analytics Engineering creates production-quality data foundations and products, including canonical datasets, shared metrics, semantic layers, and systems for quality, observability, lineage, and evaluation. The position supports self-service analytics for Finance, Legal, Operations, Marketing, Research, and other business teams. Scribd Flex offers a flexible work model, with employees choosing a workstyle and location that support their performance. Occasional in-person attendance is required for all employees, regardless of location. Employees must have a primary residence in or near one of the listed U.S., Canadian, or Mexican cities. The role is eligible for competitive equity ownership and a comprehensive benefits package.

Requirements

At least 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 with authoritative, discoverable definitions for core business concepts.
Experience designing systems for data quality, observability, documentation, governance, access control, privacy, lineage, and evaluation.
Experience designing and building canonical data models and semantic layers that support self-service analytics at scale.
Advanced SQL skills.
Production experience with a 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 investment 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 to improve data quality, security, and governance.
Experience leading on-call rotations, coordinating incident response, resolving production issues, and driving follow-up improvements to strengthen system reliability.

Responsibilities

Shape Analytics Engineering technical foundations, modeling standards, and development practices.
Build and guide governed canonical data models and semantic layers that give core business concepts authoritative, discoverable definitions.
Design data quality, observability, documentation, governance, access control, privacy, lineage, and evaluation systems for reliable and safe data and AI products.
Build data models and semantic layers that support self-service analytics at scale across business functions.
Lead cross-functional data initiatives from strategy through production adoption and measurable business impact.
Connect Analytics Engineering investments to company priorities and communicate technical tradeoffs to senior leaders.
Guide decisions about data modeling, architecture, governance, quality, tooling, and developer experience.
Balance immediate stakeholder needs with reusable data products, platform health, and technical debt reduction.
Apply AI-assisted development, analysis, agents, and automated workflows where they create clear and measurable value.
Provide early, transparent communication and thoughtful feedback to help teams make decisions and execute confidently.

Benefits

Competitive equity ownership.
Comprehensive health coverage.
Dental coverage.
Vision coverage.
Mental health support.
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.
Learning and development programs and professional growth opportunities.
Wellness stipends.
Home office stipends.
Complimentary access to the Scribd, Inc. suite of products.
Enterprise access to leading AI tools.
Flexible work model (Scribd Flex).