Data Engineer, Finance

Superhuman is hiring a Data Engineer, Finance to design and own scalable data pipelines, datasets, and models used for revenue reporting within the Finance & Revenue team. This role works closely with Finance, Revenue Operations, Analytics, and Engineering to convert billing, bookings, customer, and product-usage signals into trusted metrics such as ARR and NRR.

Responsibilities

• 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 ensure a consistent and trusted view of financial performance.
• Support the revenue attribution model by building and maintaining underlying datasets that connect product usage, customer lifecycle, and commercial signals into an explainable view of business performance.
• Model revenue data into clean, documented, reusable tables that Finance, Revenue Operations, analysts, and business partners can self-serve from.
• Own data quality, freshness, and reliability for revenue-critical datasets using automated checks, monitoring, alerting, and reconciliation processes.
• 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.

Requirements

• 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 proficiency in SQL and solid data engineering foundations, with hands-on experience in Spark and a modern lakehouse or cloud data warehouse (including Databricks, Delta Lake, dbt, Snowflake, or similar).
• Strong data modeling and data warehouse design skills, including the ability to translate complex business processes and source-system data into clear, reliable, reusable datasets.
• High standards for data quality, precision, observability, and reconciliation, especially for datasets used in revenue reporting and business decision-making.
• Experience with workflow orchestration and CI/CD for data, such as Databricks Workflows or Airflow, with Git-based deployment.
• Comfort using AI-assisted development tools like Codex or Claude Code, along with judgment to validate and supervise outputs.
• Clear communication and collaboration across business partners, analysts, engineers, and leadership, including translating between technical and business audiences.
• Strong interest in business impact, with the ability to turn ambiguous finance and revenue questions into reliable, scalable data products and metrics.
• Self-starting problem-solving approach, ability to manage priorities across multiple projects, and comfort 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

Salary range (annual): USD 157,000 - 220,500 per year.

• US Zone 1: 175,000 to 245,000
• US Zone 2: 157,000 to 220,500

Superhuman takes a market-based approach to compensation, so base pay may vary by location. Expected ranges may be modified in the future.

• Excellent health care (including medical, dental, vision, mental health, and fertility benefits)
• Disability and life insurance options
• 401(k) matching
• Paid parental leave
• 20 days of paid time off per year, 12 days of paid holidays per year, two floating holidays per year, 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, including metrics such as ARR, NRR, bookings, billing, or revenue attribution
• Experience ingesting or modeling data from Stripe or similar billing, payments, ERP, or subscription-management systems
• Experience working with product-usage data, usage-based billing, or attribution models
• A track record of building well-documented, self-serve data products used by business and analytics teams

Location and Employment Details

• Location: San Francisco, CA (hybrid)
• Hub locations: San Francisco; Seattle
• Employment type: Full time
• Location type: Hybrid
• Department: Engineering, Product, Design, and Marketing (Engineering - Data / Data/Analytics Engineer)