Lead Analytics Engineer — Payments, Identity & Trust and Safety

Scribd•San Francisco, CA

About The Position

Scribd, Inc. is hiring a Lead Analytics Engineer to support the pillars responsible for payments, identity, security, and trust and safety. This is the senior-most analytics engineering role for these areas and is a purely technical leadership position with no direct reports. The role reports to the Director, Analytics Engineering and works closely with Product Analytics, Data Science, Product, and Engineering partners. The role’s mandate is to establish authoritative definitions for core business concepts and ensure metrics remain reliable through processor migrations, schema changes, and launches in new markets. For Trust & Safety, the role will develop comparable measures of violating content prevalence and user exposure as moderation models, policies, and review processes evolve. Metric definitions are treated as engineering artifacts, with emphasis on lineage, observability, and data quality. The Lead Analytics Engineer will build canonical data models for payments, billing, and subscriptions in Databricks across Scribd, Everand, and Fable; own the KPI and semantic layer for payments and identity; and extend modeling and measurement practices to Trust & Safety. The role will improve data quality, observability, and lineage; investigate discontinuities and distinguish actual customer or processor behavior from migration artifacts and data-quality problems; and build AI tools grounded in certified metrics and tables, with evaluation sets to validate their answers. The position will also establish modeling and metric conventions, influence source-system changes, and mentor analysts and analytics engineers. Scribd, Inc. uses a flexible work model that allows employees to choose a workstyle and location, while requiring occasional in-person attendance for all employees. Employees must reside in or near an eligible city listed in the posting. 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.
Deep, hands-on payments expertise, including direct experience with processor data from Stripe, Adyen, Braintree, PayPal, or similar providers.
Practical experience with identity and authentication data.
Depth in governed canonical data models and semantic layers.
A track record of defining metrics, defending them under scrutiny, and versioning them when their inputs change.
Advanced SQL and production experience with Databricks, AWS, Looker, Airbyte, Fivetran, or comparable platforms, including orchestration, transformation frameworks, and automated testing.
Experience making data reliable for AI consumption.
Proven ability to influence senior product and engineering leaders and translate complex payment economics into clear business trade-offs.

Nice To Haves

Advanced Python and experience building AI agents or LLM-based pipelines.
Experience with Trust & Safety or integrity measurement.
Experience with local payment methods across LATAM, APAC, or EU markets, and with stored-credential and network compliance frameworks.
Experience with on-call rotations and incident response for production data systems.

Responsibilities

Own canonical data models for payments, billing, and subscriptions in Databricks across Scribd, Everand, and Fable, designed to accommodate new markets and payment methods.
Own the KPI and semantic layer for payments and identity, exposed through self-serve dashboards and AI tools.
Own Trust & Safety models and metrics for content prevalence and exposure, including evaluation of AI-generated moderation labels.
Drive data quality, observability, and lineage so changes in login challenge rates or processor routing are detected promptly.
Investigate metric discontinuities and identify whether they result from actual customer or processor behavior, migration artifacts, or data-quality issues.
Build AI tools on trusted foundations using certified metrics and tables, keep them synchronized with definition changes, and validate them with evaluation sets.
Set modeling and metric conventions and influence source-system changes before they are implemented.
Mentor analysts and analytics engineers working in these domains.
Document methodology and models, and communicate clearly and early with partners.

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.