Data Engineer, Finance

Role details

Superhuman offers a dynamic hybrid working model for this role. This flexible approach gives team members the best of both worlds: plenty of focus time along with in-person collaboration that helps foster trust, innovation, and a strong team culture. Preferred locations for team members for our Data Platform roles are our San Francisco or Seattle hubs. ABOUT SUPERHUMAN Grammarly is now part of Superhuman, the AI productivity platform on a mission to unlock the superhuman potential in everyone. The Superhuman suite of apps and agents brings AI wherever people work, integrating with over 1 million applications and websites. The company's products include Grammarly's writing assistance, Docs’ collaborative workspace, Mail's inbox management, and Go, the proactive AI assistant that understands context and delivers help automatically. Founded in 2009, Superhuman empowers over 40 million people, 50,000 organizations, and 3,000 educational institutions worldwide to eliminate busywork and focus on what matters. Learn more at superhuman.com http://superhuman.com and about our values here https://superhuman.com/company/values. The Opportunity Want to build the data foundations behind how Superhuman measures and grows its business? As a Data Engineer on the Finance & Revenue team, you’ll own the pipelines, models, and datasets that power revenue reporting across Superhuman’s AI-native productivity platform: Grammarly’s writing assistance, Mail, Docs, Databases, and Go. You’ll partner closely with Finance, Revenue Operations, Analytics, and Engineering to turn billing, bookings, customer, and product-usage signals into reliable, decision-grade data. Superhuman is a compound startup: we build many products as one integrated suite rather than standalone tools. That model creates an unusually rich and complex revenue data opportunity, with signals spanning billing systems, self-serve and enterprise motions, and cross-product customer usage. The data you model connects those surfaces to create a unified view of ARR, NRR, bookings, and revenue performance across the Superhuman Suite. This is a high-ownership, high-impact role at the intersection of data engineering, finance, and business strategy. You’ll own revenue-critical systems end-to-end and contribute to the revenue attribution model, ensuring the company’s most visible financial datasets are precise, accessible, and trusted. Your work will directly shape how leadership understands business performance and makes investment decisions. What you’ll do - Design, build, and own scalable data pipelines (Spark/Databricks) that ingest and model billing, subscription, payment, and bookings data across the Superhuman Suite. - Build and maintain the foundational datasets for ARR, NRR, bookings, and other revenue metrics, ensuring the company has a consistent, trusted view of financial performance. - Contribute to the revenue attribution model by building and maintaining the underlying datasets that connect product usage, customer lifecycle, and commercial signals into an explainable view of business performance. - Model revenue data into clean, well-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, with 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 the performance, cost efficiency, and developer experience of our finance and revenue data platform. You’ll do this alongside partners across Finance, Revenue Operations, Analytics Engineering, Product, and Engineering. We think from first principles, challenging the familiar to reframe problems and reach sharper solutions, and win with grit, staying with the hardest problems through the messy middle. You’ll have the freedom to own your systems and directly influence the roadmap, and the complexity of what you build will grow quickly as we scale. QUALIFICATIONS - You have 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. - You’re highly proficient in SQL and have strong data engineering foundations, with hands-on experience in Spark and a modern lakehouse or cloud data warehouse (Databricks, Delta Lake, dbt, Snowflake, or similar). - You have strong data modeling and data warehouse design skills, with the ability to transform complex business processes and source-system data into clear, reliable, and reusable datasets. - You bring a rigorous approach to data quality, precision, observability, and reconciliation, especially for datasets used in revenue reporting and business decisions. - You have experience with workflow orchestration and CI/CD for data (for example, Databricks Workflows or Airflow, with Git-based deployment). - You’re comfortable using AI-assisted development tools like Codex or Claude Code to move faster, and you have the judgment to validate and supervise their output. - You communicate clearly and collaborate effectively with business partners, analysts, engineers, and leadership, translating smoothly between technical and business audiences. - You care about business impact and enjoy turning ambiguous finance and revenue questions into reliable, scalable data products and trustworthy metrics. - You’re a self-starting problem-solver who thinks from first principles, manages priorities across multiple projects, and thrives in a fast-paced, results-driven environment. 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 billi