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Salary: USD 175,000 - 245,000 / annual
Superhuman (which includes Grammarly, Mail, Docs, and Go) is seeking a Data Engineer to own the data foundations powering revenue reporting and financial analysis across its AI-native productivity platform. This is a high-ownership role at the intersection of data engineering, finance, and business strategy.
You will design, build, and operate scalable data pipelines using Spark and Databricks to ingest and model billing, subscription, payment, and bookings data. You'll own the foundational datasets for ARR, NRR, bookings, and other revenue metrics, ensuring the company has a consistent, trusted view of financial performance. A key responsibility is contributing to the revenue attribution model by building datasets that connect product usage, customer lifecycle, and commercial signals into an explainable view of business performance.
You'll model revenue data into clean, well-documented, reusable tables that Finance, Revenue Operations, analysts, and business partners can self-serve from. Data quality, freshness, and reliability for revenue-critical datasets will be your ownership area, including automated checks, monitoring, alerting, and reconciliation processes. You'll partner closely with Finance, Revenue Operations, Analytics Engineering, Product, and Engineering to translate business questions into robust data models and trustworthy metrics.
Superhuman operates a compound startup model, building many products as one integrated suite. This creates an unusually rich and complex revenue data opportunity, with signals spanning billing systems, self-serve and enterprise motions, and cross-product customer usage. Your work will directly shape how leadership understands business performance and makes investment decisions.
The role offers a hybrid working model with preferred locations in San Francisco or Seattle hubs, providing focus time and in-person collaboration.
REQUIREMENTS:
- 3+ years of experience building and operating production data pipelines and data platforms, ideally supporting finance, revenue, billing, or other business-critical analytical use cases
- Highly proficient in SQL with strong data engineering foundations
- Hands-on experience in Spark and a modern lakehouse or cloud data warehouse (Databricks, Delta Lake, dbt, Snowflake, or similar)
- Strong data modeling and data warehouse design skills, with ability to transform complex business processes and source-system data into clear, reliable, and reusable datasets
- 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 (e.g., Databricks Workflows or Airflow, with Git-based deployment)
- Comfortable using AI-assisted development tools like Codex or Claude Code to move faster, with judgment to validate and supervise their output
- Clear communication and effective collaboration with business partners, analysts, engineers, and leadership
- Care about business impact and ability to turn ambiguous finance and revenue questions into reliable, scalable data products
- Self-starting problem-solver who thinks from first principles, manages priorities across multiple projects, and thrives in fast-paced, results-driven environments
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
- Track record of building well-documented, self-serve data products that business and analytics teams rely on
