Data Engineer, Finance

Superhuman is looking for a Data Engineer, Finance to help build and operate the scalable data pipelines, datasets, and models that power revenue reporting. On the Finance & Revenue team, you will partner across Finance, Revenue Operations, Analytics, and Engineering to turn billing, bookings, customer, and product-usage signals into metrics teams can trust, including ARR and NRR.

This role is based in a hybrid setup in the San Francisco, CA hub, with an additional hub location in Seattle. The position is full time, with an expected experience level of 3+ years and salary ranges that vary by compensation zone.

What you’ll do

• Design, build, and own scalable data pipelines using Spark and Databricks to ingest and model billing, subscription, payment, and bookings data across the Superhuman Suite.
• Build and maintain foundational datasets for ARR, NRR, bookings, and other revenue metrics to support a consistent, trusted view of financial performance.
• Support a revenue attribution model by creating and maintaining datasets that connect product usage, customer lifecycle, and commercial signals into an explainable business performance view.
• Model revenue data into clean, well-documented, reusable tables so Finance, Revenue Operations, analysts, and business partners can self-serve.
• Own data quality, freshness, and reliability for revenue-critical datasets through automated checks, monitoring, alerting, and reconciliation.
• Partner with Finance, Revenue Operations, Analytics Engineering, Product, and Engineering to translate business questions into robust data models and trustworthy metrics.
• Continuously improve performance, cost efficiency, and developer experience for the finance and revenue data platform.

What you bring

• 3+ years of experience building and operating production data pipelines and data platforms, ideally for finance, revenue, billing, or other business-critical analytical use cases.
• Strong SQL skills and solid data engineering foundations, with hands-on experience in Spark and a lakehouse or cloud data warehouse such as Databricks, Delta Lake, dbt, Snowflake, or similar.
• Strong data modeling and data warehouse design capability to transform complex business processes and source-system data into clear, reliable, reusable datasets.
• A rigorous approach to data quality, precision, observability, and reconciliation, especially for datasets used in revenue reporting and business decisions.
• Experience with workflow orchestration and CI/CD for data (for example, Databricks Workflows or Airflow) with Git-based deployment.
• Comfort using AI-assisted development tools like Codex or Claude Code, with judgment to validate and supervise outputs.
• Clear communication and collaboration across business partners, analysts, engineers, and leadership, translating between technical and business audiences.
• Interest in business impact and the ability to turn ambiguous finance and revenue questions into reliable, scalable data products and metrics.
• Self-starting problem-solving, prioritization across multiple projects, and effectiveness in a fast-paced, results-driven environment.

Technologies

• SQL
• Spark
• Databricks
• Delta Lake
• dbt
• Snowflake
• Databricks Workflows
• Airflow
• Git
• Codex
• Claude Code

Compensation and benefits

• Superhuman uses a market-based approach to compensation; base pay may vary by location.
• US Zone 1 salary range: 175,000 to 245,000
• US Zone 2 salary range: 157,000 to 220,500
• Excellent health care (medical, dental, vision, mental health, and fertility benefits)
• Disability and life insurance options
• 401(k) matching
• Paid parental leave
• Paid time off: 20 days plus 12 paid holidays, two floating holidays, and flexible sick time
• Generous stipends (caregiving, pet care, wellness, home office, and more)
• Annual professional development budget and opportunities

Nice to have

• Direct experience supporting Finance, Revenue Operations, or revenue analytics (ARR, NRR, bookings, billing, revenue attribution).
• Experience ingesting or modeling data from Stripe or similar systems (billing, payments, ERP, subscription management).
• Experience with product-usage data, usage-based billing, or attribution models.
• A track record of building well-documented, self-serve data products relied on by business and analytics teams.