Staff Analytics Engineer, Affirm

• 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

Desirable

• 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
• 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