Superhuman is seeking a Data Engineer, Finance to help build and operate scalable data pipelines, datasets, and models used for revenue reporting. In the Finance & Revenue organization, this role partners across Finance, Revenue Operations, Analytics, and Engineering to turn billing, bookings, customer, and product usage signals into trusted metrics such as ARR and NRR.
This hybrid position is based in the San Francisco, CA hub (with another hub listed in the process) and focuses on creating decision-grade, self-serve data foundations that revenue teams can rely on. The role also contributes to revenue attribution by linking product activity and lifecycle signals to commercial performance in an explainable way.
What you’ll do
- Design, build, and take ownership of scalable data pipelines using Spark/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 provide a consistent view of financial performance.
- Support the revenue attribution model by building and maintaining underlying datasets that connect product usage, customer lifecycle, and commercial signals.
- Create clean, well-documented, reusable revenue tables so Finance, Revenue Operations, analysts, and business stakeholders can self-serve metrics.
- Own data quality, freshness, and reliability for revenue-critical datasets, including automated checks, monitoring, alerting, and reconciliation.
- Collaborate with Finance, Revenue Operations, Analytics Engineering, Product, and Engineering to translate business questions into robust models and trustworthy metrics.
- Continuously improve the performance, cost efficiency, and developer experience of the finance and revenue data platform.
Required qualifications
- 3+ years 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 hands-on experience with Spark plus a modern lakehouse or cloud data warehouse such as Databricks, Delta Lake, dbt, Snowflake, or similar technologies.
- Solid data modeling and warehouse design skills, with the ability to turn complex business processes and source-system data into clear, reliable, reusable datasets.
- A rigorous approach to data quality, precision, observability, and reconciliation 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 such as Codex or Claude Code, along with judgment to validate and supervise outputs.
- Clear communication and effective collaboration with business partners, analysts, engineers, and leadership across technical and business audiences.
- Strong interest in business impact and in turning ambiguous finance and revenue questions into scalable data products and trusted metrics.
- Self-starting problem-solving, ability to prioritize across multiple projects, and comfort working in a fast-paced, results-driven environment.
Technologies
- SQL
- Spark
- Databricks
- Delta Lake
- dbt
- Snowflake
- Databricks Workflows
- Airflow
- Git
- Codex
- Claude Code
Benefits
- Excellent health care (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, including those for caregiving, pet care, wellness, and your home office
- Annual professional development budget and opportunities
Nice to have
- 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 relied on by business and analytics teams.
Compensation and location details
Superhuman uses a market-based approach to compensation, with base pay varying by location. The expected salary range for this role is USD 157,000 to 220,500 per year. In the US, two compensation zones are listed: US Zone 1: 175,000 to 245,000 and US Zone 2: 157,000 to 220,500.
This role is full-time and hybrid, with hubs listed as San Francisco and Seattle.
