Lead Analytics Engineer Identity & Trust and Safety

Scribd, Inc. is on a mission to advance human understanding. Our four products — Scribd®, Slideshare®, Everand™, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise.

We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer.

We believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture. Occasional in-person attendance is required for all Scribd, Inc. employees, regardless of location.

Data & Analytics is building the DNA of an AI native Scribd, Inc. Bringing together Analytics Engineering, Data Science, and Data Analytics, D&A turns fragmented data and inconsistent definitions into trusted, production-quality data foundations — and builds the AI agents and analytical tools that bring reliable intelligence directly into how teams make decisions.

This role supports the pillars that own payments, identity, security as well as trust & safety systems — the foundations that let Scribd securely monetize and protect its products. It is the senior-most analytics engineering role for these pillars and a purely technical leadership position with no direct reports. You will report to the Director, Analytics Engineering, and work embedded alongside Product Analytics, Data Science, Product, and Engineering partners.

Give core business concepts one authoritative definition, and make sure the numbers survive a PSP migration, a schema change, or a new market launch without silently going wrong. For Trust & Safety, make it possible to say with confidence how much violating content exists on Scribd and how much of it users see with numbers that stay comparable as moderation models, policies, and review processes change.

A metric definition is an engineering artifact. We care about lineage, observability, and data quality as much as the dashboard on top — because a number that quietly breaks upstream is worse than no number at all.

• 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.

• 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…