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.Culture at Scribd, Inc.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.About The Team And RoleThe team.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.The role.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.Mandate.Give core business concepts one authoritative definition, and make sure the numbers survive a PSP migration, a schema change, or a recent 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.Core philosophy.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.About YouYou 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

📌 Lead Analytics Engineer — Payments, Identity & Trust And Safety (Toronto)
🏢 Scribd
📍 Toronto