Lead Analytics Engineer, AI Native Self Service Analytics

Scribd, Inc.•Vancouver, BC

3d•$149,000 - $253,500

About The Position

Scribd, Inc. is hiring a Principal/Lead Analytics Engineer to help scale data for an AI-native organization. The role is a senior individual-contributor position focused on shaping the technical foundations, modeling standards, and development practices that enable Analytics Engineering to move faster with greater confidence. The engineer will provide technical leadership, connect investments to company priorities, communicate tradeoffs to senior leaders, and contribute to decisions about data modeling, architecture, governance, quality, tooling, and developer experience. The role calls for pragmatic, sequenced improvements rather than waiting for an ideal end state, with early and transparent communication to build trust and help teams move confidently. It also involves applying AI technologies—including agentic coding, AI-assisted development and analysis, and automated workflows—where they create clear, measurable value. Success means strengthening Analytics Engineering through data systems that are reliable, understandable, scalable, and useful, while making teams more capable through technical leadership. Analytics Engineering works with Engineering and partners across product, technology, content, and business teams. The team creates trusted, reusable, production-quality data foundations and products, including canonical datasets, shared metrics, semantic layers, and systems for quality, observability, lineage, and evaluation. The goal is to make reliable intelligence available in company decisions and workflows.

Requirements

At least 8 years of experience in analytics engineering, data engineering, business intelligence engineering, or a related discipline; the posting also states “10/8+ years.”
Depth in building governed canonical data models and semantic layers that establish authoritative, discoverable definitions of core business concepts.
Experience designing data quality, observability, documentation, governance, access control, privacy, lineage, and evaluation systems for reliable, explainable, trustworthy, and safe data and AI products.
Experience designing and building canonical data models and semantic layers that support self-service analytics at scale for Finance, Legal, Operations, Marketing, Research, and other business teams.
Advanced SQL skills and 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 balancing immediate stakeholder needs with long-term investments in reusable data products, platform health, and technical debt reduction.
Excellent written and verbal communication skills 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.
Provide hands-on technical leadership and guide technical decisions across teams.
Connect Analytics Engineering investments to company priorities and communicate tradeoffs to senior leaders.
Contribute to decisions regarding data modeling, architecture, governance, quality, tooling, and developer experience.
Design and build governed canonical data models and semantic layers for scalable self-service analytics.
Design systems for data quality, observability, documentation, governance, access control, privacy, lineage, and evaluation.
Lead cross-functional data initiatives from strategy through production adoption and measurable business impact.
Balance immediate stakeholder needs with investments in reusable data products, platform health, and technical debt reduction.
Apply AI-assisted development, analysis, agents, and automated workflows where they deliver measurable value.
Strengthen the reliability, clarity, scalability, and usefulness of data systems and improve team capability through technical leadership.

Benefits

Competitive equity ownership.
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.
Learning and development programs and professional growth opportunities.
Wellness and home office stipends.
Complimentary access to the Scribd, Inc. suite of products.
Enterprise access to leading AI tools.
Scribd Flex flexible work model.