Financial Systems Data Engineer

Angel is changing the future of entertainment and is one of the fastest-growing distributors. Our rapidly expanding library of light-amplifying stories has grown 10x in under 2 years. Gone is the old model where the deepest pockets pick the stories we share. Angel restores choice to our 2 million guild members who decide what we produce, what we take to theaters, and most importantly what parents bring to their homes. Check out angel.com/watch

You’ll take ownership of Angel’s financial data foundation

You’ll take ownership of how Angel understands, reconciles, and operationalizes its financial data—building the datasets, integrations, and metric definitions that make our numbers accurate, traceable, and actionable. From Stripe and Shopify through NetSuite and into Snowflake, you’ll ensure financial transactions are auditable from source events to journal-ready outputs, automate the workflows that keep Finance moving fast, and create a financial data foundation the company can rely on as we scale in a public-company environment.

What You'll Do

As our Financial Systems Data Engineer, you’ll work directly with our finance team to turn finance and operational data into trusted datasets, automated integrations, and clear reporting that helps Angel run with accuracy and speed. You’ll work with focused depth—so you can deliver high-velocity, high-impact work on financial data foundations, reconciliations, and the metrics that power decision-making.

This is a hub-and-spoke role: you’ll be a core, cross-functional member of the Data team, embedded day-to-day with Finance to accelerate outcomes, improve data quality at the source, and make financial metrics consistent and auditable.

• Design and maintain financial data pipelines — build and maintain pipelines that ingest, normalize, and reconcile data across payment, commerce, and accounting systems (Stripe, Shopify, NetSuite, etc.), ensuring transactions are traceable from source events through ledger/journal-ready outputs. Monitor and maintain pipeline reliability by handling upstream scheme changes, data anomalies and operational issues to ensure consistent data delivery.
• Model end-to-end transaction lifecycles — reconstruct and model complex financial flows (payments, refunds, fees, taxes, adjustments, chargebacks) including edge cases like prorated refunds, gross vs. net logic, and tax jurisdiction nuances—translating them into auditable datasets suitable for accounting and reporting.
• Build trusted financial datasets — write complex SQL in Snowflake (and dbt where appropriate) to create clean, reusable datasets that power reporting, reconciliations, forecasting support, and ad hoc analysis.
• Own cross-system integrations and identifiers — use Python to build and maintain integrations (REST APIs and event-driven workflows where applicable), including schema change handling, metadata mapping, and reconciliation of cross-system identifiers.
• Investigate and resolve discrepancies — trace transactions across multiple systems and data sources to identify root causes, resolve mismatches, and improve upstream data correctness and monitoring.
• Establish trust through financial metrics — define, refine, and communicate the metrics that matter (revenue, refunds/chargebacks, COGS, margin, deferred revenue concepts, cash timing), building stakeholder confidence in how we measure performance.
• Use AI to move faster (without lowering the bar) — leverage AI tools to accelerate SQL/dbt development, debugging, documentation, testing, and carefully scoped workflow automation, while maintaining high standards for correctness, traceability, and security.
  
  

What Success Looks Like

• Finance and leadership trust the numbers—your Snowflake datasets (and dbt models where used) become the go-to source for financial reporting and analysis.
• Financial transactions are traceable end-to-end from Stripe/Shopify source events to NetSuite-ready outputs, with clear documentation and reconciliation paths.
• Manual reconciliations and recurring reporting tasks are automated, reducing close time and operational risk.
• Discrepancies across systems are identified earlier, debugged faster, and fixed at the root—improving correctness and confidence.
• Teams across the organization confidently use the metrics and data foundations you’ve built in Rill, Lightdash, and Metabase.