Manager, Analytics Engineering (Finance Data)

Our Financial Systems team is looking for a Manager, Analytics Engineering to lead a team that turns complex finance data into reliable, governed, audit-ready reporting foundations. You will own end-to-end delivery of governed data models and the semantic layer that power reconciliations, close, and external reporting automation at Affirm. We're looking for someone who can lead, execute, and partner deeply across Accounting, Finance, and Data Engineering—operating as a hands-on technical leader to improve controls, data quality, and month-end outcomes at scale. The ideal candidate will deliver impact through scalable semantic layers and reporting solutions that elevate Finance function outcomes.

What You'll Do

• Lead end-to-end delivery of finance data models that support reconciliations, journal entry preparation, and close workflows, with a focus on reliability, controls, and audit readiness.

• Own the Financial Reporting semantic layer strategy and execution, including definition governance and adoption across reporting surfaces.

• Drive migration and modernization of reconciliation and close-support reporting into standardized, automated outputs where appropriate.

• Partner with Accounting and Finance stakeholders to translate close and audit requirements into durable data contracts, model specifications, and delivery roadmaps.

• Build and evolve reporting data products that make financial insights easy to consume and hard to misinterpret, partnering with BI and Financial Reporting tools (for example Sigma and Workiva).

• Build the foundations for AI-assisted finance analytics by enabling AI agents to safely access governed finance datasets (for example, through well-defined metrics, strong documentation, and permissioned datasets) to support self-service questions and reporting workflows.

• Establish strong analytics engineering practices across the stack (testing, documentation and glossary stewardship, monitoring/alerting, code review, release discipline, and operational ownership).

What We Look For

• Strong problem-solving and systems thinking: able to understand end-to-end accounting scenarios and translate them into well-structured data models that are clear, reliable, and easy to maintain.

• Experience working in audit and controls-driven environments (for example SOX-relevant processes), with an understanding of how data quality, lineage, and evidence tie to financial reporting outcomes.

• Ability to translate accounting and controls requirements into clear data contracts (inputs, transformations, definitions, ownership, and expected outputs) that can be implemented and operated reliably.

• Self-starter and self-learner: takes ownership, ramps quickly in new domains, and prioritizes durable solutions over attachment to any specific tool or approach.

• Strong experience delivering analytics engineering or data engineering initiatives end-to-end in a complex environment, including cross-functional alignment.

• Ability to influence without authority across Accounting, Finance Systems, and Engineering to drive decisions, alignment, and delivery.

• Proven ability to build reporting data products for business users (for example, curated datasets and governed metrics that power BI and external reporting workflows), partnering effectively with BI and financial reporting tooling teams.

• Deep experience with modern data transformation and warehousing patterns (for example dbt and Snowflake), including production operations and reliability practices.