Analytics Engineer

About Highnote

Founded in 2020 by a team of leaders from Braintree, Pay Pal, and Lending Club, Highnote is an embedded finance company that sets the standard in modern card platform management. As an all-in-one card issuer processor and program management platform, we provide digital-first organizations with the flexibility to seamlessly issue and process payment cards, embed virtual and physical card payments, and integrate ledger and wallet functionalities—empowering businesses to drive growth and profitability.

We’ve raised $145M+ and have grown our team to 140+ employees. Headquartered in San Francisco, we’ve managed to build one of the most advanced payments teams in the industry, with team members in 25+ US states.

Operating through our core values of customer obsession, executional excellence, intentional inclusion, we’re helping businesses grow for the future by creating the payment products demanded by tomorrow, with the ability to solve for use cases that don’t exist yet.

We are fast-moving, hands‑on, and strongly believe everyone deserves a seat at the table. We believe we’re unlocking incredible opportunities that can change the future of payments, as long as we have the right people to make it happen.

As a Payment Analytics Engineer reporting to the Director of Business Intelligence, you will be a key technical partner for our Partner Operations team. Your mission is to ensure absolute data consistency across the entire payment lifecycle, protecting the company from unknown liabilities while optimizing revenue through deep interchange analysis and forecasting. While you will not be responsible for the raw ingestion of card network data, you must possess a deep understanding of the ingestion process to track and audit data as it moves through our platform.

You will interpret complex card network file specifications and our internal processing logic to ensure correct reporting on every transaction.

• Network Reporting & Regulatory Compliance
  • Decipher & Map Specifications:
    Translate dense card network jargon into Highnote‑specific event codes and logic.
  • Automate Periodic Reporting:
    Own the end‑to‑end automation of quarterly network reporting and validation, ensuring submissions meet strict compliance standards with evolving requirements.
  • Point‑in‑Time Integrity:
    Architect systems to maintain rigorous point‑in‑time snapshots, ensuring data quality over time.
  • Logic Lifecycle Management:
    Proactively track and incorporate quarterly network specification changes to prevent reporting gaps or inaccuracies.
• Interchange Analysis & Revenue Attribution
  • Interchange Optimization:
    Analyze transaction‑level data to identify revenue leakage or downgrades where transactions failed to qualify for optimal interchange rates.
  • Granular Attribution:
    Build models to attribute complex network assessment fees and interchange revenue and costs back to specific customers, bin ranges, and product types.
  • Fee Validation:
    Perform deep‑dives into network invoices to ensure Highnote is receiving all eligible incentives and that customer billing accurately reflects network costs.
• Strategic Modeling & Network Economics
  • Network Pricing Scenarios:
    Model the financial impact of bi‑annual network fee updates and pricing changes, allowing the business to adjust strategies before changes go live.
  • Product vs. Cost Comparisons:
    Conduct deep‑dive analyses comparing the financial impact of different interchange qualifications to quantify product development efforts.
  • Margin Impact Analysis:
    Evaluate how specific network rule changes affect overall margin and product profitability.
• Data Engineering & Upstream Influence
  • Upstream Logic Migration:
    Partner with core Engineering to migrate complex financial logic from downstream dashboards into upstream denormalized tables and User Defined Functions (UDFs).
  • Dynamic Data Modeling:
    Design and optimize SQL/No
    
    SQL structures that handle high‑volume transactional data and automate bin‑range table updates.

• Experience:
  
  8+ years in data engineering, analytics, or reporting; with 5+ years of experience in Payments and Fintech.
• Domain Expertise:
  Deep…