Our four products — Scribd®, Slideshare®, Everand™, and Fable — help billions of people across the globe move beyond access and into insight, application, and expertise. 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. 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. 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 can build the pipeline, define the metric, and defend the number in a room full of executives. 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 ask what the right next version is,



and you use AI where it creates measurable value. 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. 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. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. In Canada, the reasonably expected salary range is between $178,000 CAD[minimum salary in our lowest geographic market] to $227,000 CAD[maximum salary in our highest geographic market]. relevant education or training; and other business and organizational needs.

In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.

Scribd

Flex (flexible work model) 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 and employee equity Wellness and home office stipends Enterprise access to leading AI tools If you apply for a job with Scribd, Inc. or otherwise engage with us in connection with employment (including as an employee, contractor, or other personnel), the personal information we process in that context is subject to our Employee and Applicant Privacy Policy, which is available here. Scribd, Inc. is committed to equal employment chance regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas.

Come join us in building something meaningful. #

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