Staff Analytics Engineer, Subledger Platform

Affirm is reinventing credit to make it more honest and friendly, giving consumers the flexibility to buy now and pay later without any hidden fees or compounding interest. About the Finance team The Finance team ensures Affirm remains financially sound and strategically positioned for growth. Our team manages financial planning, accounting, pricing, vendor management, tax, investor relations, and corporate development. We deliver timely insights, accurate reporting, and careful analysis to support decision-making. From day-to-day financial operations to major investments, we enable sustainable, informed growth by maintaining strong fiscal discipline. About the team Financial Systems owns the data and reporting foundation for Accounting, and the operational reliability of the pipelines that power reporting, reconciliations, and automation. We are building and scaling a single source of truth for financial information using dbt and Snowflake to enable scalable BI, close workflows, controls, and automation across the org. We’re looking for a Staff Analytics Engineer to build and own our Financial Subledger Data Platform—the canonical set of dbt models, governed datasets, and control frameworks that transform operational/ledger events into trusted, explainable, accounting-ready outputs. This is a senior hands-on role: you will write and ship production dbt code yourself (models, tests, docs), set standards for the platform, and directly manage and mentor one Analytics Engineer as a player/coach. This role operates with high autonomy and significant cross-functional partnership across Accounting, Financial Reporting, Financial Systems, and upstream product engineering teams. We want someone who can maintain and harden today’s platform while also giving us flexibility to adopt new technologies (e.g., evolving warehouse patterns, new table formats, catalog/governance tooling, orchestration approaches) as our subledger and control needs scale. What you’ll do - Build and own dbt models for the financial subledger platform (staging → intermediate → canonical facts/balances/marts → semantic), including naming conventions, macros, and reusable patterns. - Implement strong data quality and controls in dbt: tests (unit/relationship/assertions), freshness, anomaly checks, and automated reconciliations that support close and audit readiness. - Embed AI-assisted reconciliation capabilities into the platform (within appropriate security/controls guardrails): automate variance triage, suggest likely root causes, and generate human-reviewable reconciliation narratives and evidence artifacts. - Own end-to-end subledger data products (e.g., event/fact layers, balance rollforwards, reconciliation outputs) with traceability from source events through transformations to reporting outputs. - Partner with Accounting/Financial Reporting to translate requirements into clear model specifications (definitions, posting logic assumptions, tie-out rules) and ship them as durable dbt assets. - Drive production operational ownership: monitoring/alerting, incident response, root-cause fixes, and release hygiene for the pipelines and models you own. - Collaborate with upstream engineering teams to define inputs and improve source data quality via contracts and change management. - Coach and develop one Analytics Engineer (Poland) via code review, pairing, scoped ownership, and clear technical direction. What we look for - Deep, hands-on analytics engineering experience: you have built and maintained production dbt projects (models, tests, macros, documentation, CI) and can own them end-to-end. - Strong SQL and data modeling skills; experience building canonical datasets that support finance reporting, reconciliations, or other correctness-critical outcomes. - Experience operating data pipelines in a warehouse environment (Snowflake preferred): performance tuning, cost awareness, and reliability practices. - Strong programming skills (Python preferred) for building data tooling and automation (e.g., control checks, reconciliation workflows, utilities, CLIs, and integrations). - Deep Snowflake technical expertise: architecture patterns (micro-partitioning/clustering, query optimization), security/governance (RBAC, masking policies), and operational excellence (monitoring, cost management, reliability). - Strong ownership mindset and ability to lead ambiguous work through influence across Finance, Accounting, and Engineering stakeholders. - Nice to have: - Experience with accounting concepts (subledgers, journal entries/posting logic, balance rollforwards, tie-outs, close processes). - Experience designing SOX-friendly controls and producing repeatable evidence from data pipelines. - Experience applying AI/LLMs to data quality or reconciliation workflows (e.g., anomaly explanation, automated investigation summaries), with strong governance and human-in-the-loop review. - AWS experience (nice to have): S3/IAM, data

Deep, hands-on analytics engineering experience: you have built and maintained production dbt projects (models, tests, macros, documentation, CI) and can own them end-to-end. Strong SQL and data modeling skills; experience building canonical datasets that support finance reporting, reconciliations, or other correctness-critical outcomes. Experience operating data pipelines in a warehouse environment (Snowflake preferred): performance tuning, cost awareness, and reliability practices. Strong programming skills (Python preferred) for building data tooling and automation (e.g., control checks, reconciliation workflows, utilities, CLIs, and integrations). Deep Snowflake technical expertise: architecture patterns (micro-partitioning/clustering, query optimization), security/governance (RBAC, masking policies), and operational excellence (monitoring, cost management, reliability). Strong ownership mindset and ability to lead ambiguous work through influence across Finance, Accounting, and Engineering stakeholders. Nice to have: Experience with accounting concepts (subledgers, journal entries/posting logic, balance rollforwards, tie-outs, close processes). Experience designing SOX-friendly controls and producing repeatable evidence from data pipelines. Experience applying AI/LLMs to data quality or reconciliation workflows (e.g., anomaly explanation, automated investigation summaries), with strong governance and human-in-the-loop review. AWS experience (nice to have): S3/IAM, data...