Lead Analytics Engineer — Payments, Identity & Trust and Safety

Scribd, Inc. is hiring a Lead Analytics Engineer to build trusted, production-quality data foundations for payments, identity, security, and Trust & Safety. The role owns canonical data models, metrics, data quality and observability, and AI tools, while providing technical leadership across partner teams.

Responsibilities:

• Own the canonical data models for payments, billing, and subscriptions in Databricks modeled once across Scribd, Everand, and Fable so a new market, or payment method doesn't require a rewrite, and built with the next engineer and the AI tools on top as explicit consumers
• Own the KPI and semantic layer for payments and identity exposed through self-serve dashboaring/AI tools
• Extend the same discipline to Trust & Safety, owning the models and metrics behind prevalence and exposure, including evaluation of AI-generated moderation labels
• Drive quality, observability, and lineage so a regional login challenge rate change or processor routing change is caught in days, not quarters and root-cause discontinuities to separate real customer or processor behavior from migration artifacts and data quality issues
• Build AI tools on trusted foundations, grounded in certified metrics and tables, kept in sync as definitions change, and validated with evaluation sets that prove the answers are right
• Raise the technical bar, set modeling and metric conventions, influence source-system changes before they land, and mentor analysts and analytics engineers in these domains

Requirements:

• You are equally comfortable as the modeler and the analyst
• You can build the pipeline, define the metric, and defend the number in a room full of executives
• You are skeptical by default
• When payment success rate drops four points, your instinct is to go find the field that changed meaning, not to write the narrative
• You have opinions about how payments data should be structured to prevent misleading narratives
• You are pragmatic
• You ask what the right next version is, and you use AI where it creates measurable value
• You communicate early and plainly, and you write things down
• Methodology and model documentation are part of the deliverable
• 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
• Practical experience with identity and authentication data
• Depth in governed, canonical data models and semantic layers, and a track record of defining a metric, defending it under scrutiny, and versioning it when its inputs changed
• 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
• Advanced Python and experience building AI agents or LLM-based pipelines
• Experience with trust and 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