Lead Analytics Engineer — Payments, 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.

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.

So what are we looking for in new team members? At Scribd, Inc., we hire for “GRIT.” Traditionally defined as the intersection of passion and perseverance toward long-term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Innovative ideas and solutions, and strengthening the broader Team through collaboration and attitude.

This posting reflects an approved, open position within the organization.

About the team and role

The 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 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.

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 you

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.

You will

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.

You have

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.

Nice to have

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.

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At Scribd, Inc., your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States.

In the state of California, the reasonably expected salary range is between $179,000 [minimum salary in our lowest geographic market within California] to $253,500 [maximum salary in our highest geographic market within California].

In the United States, outside of California, the reasonably expected salary range is between $149,000 [minimum salary in our lowest US geographic market outside of California] to $240,500 [maximum salary in our highest US geographic market outside of California].

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].

We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. 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.

Working at Scribd, Inc.

• Are you currently based in a location where Scribd, Inc. can employ you?
• Employees must have their primary residence in or near one of the following cities. This includes surrounding metro areas or locations within a typical commuting distance:

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