> Markdown version of [/jobs/ext/4463484-lead-analytics-engineer-payments-identity-trust-and-safety](https://www.wearedevelopers.com/jobs/ext/4463484-lead-analytics-engineer-payments-identity-trust-and-safety). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Lead Analytics Engineer - Payments, Identity & Trust and Safety

- **Company:** Scribd Inc.
- **Location:** Denver, CO, United States (Remote available)
- **Experience:** Expert
- **Salary:** $149,000 - $253,500
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Automation of Tests, Information Engineering, Payment Systems, Python (Programming Language), Operational Databases, Management of Software Versions, Sql Optimization, Large Language Models, Agentic-AI, Data Layers, Stripe, Looker Analytics, Databricks
- **Published:** October 15, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c5bd8b9ef2fc67f4

## About the Role

* 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.

## Description

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., * 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 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.