Finance Revenue Data Engineer

Superhuman; San Francisco (CA); Hybrid; USD 157,000 - 245,000; Full time

Benefits offered by this job

Excellent health care

Disability and life insurance options

401(k) matching

Paid parental leave

20 days of paid time off, 12 holidays,

two floating holidays, and flexible S

Generous stipends for caregiving, pet,

home office and wellness

Annual professional development budget

Job summary

Superhuman is seeking a Data Engineer on the Finance & Revenue team to own revenue data pipelines and datasets across the Grammarly-powered suite. You’ll partner with Finance, Revenue Operations, Analytics, and Engineering to turn billing, bookings, customer, and product-usage signals into reliable, decision-grade data.

This high-ownership role designs scalable pipelines (Spark/Databricks), maintains ARR/NRR datasets, supports the revenue attribution model, and ensures data quality with

Qualifications

• 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 and 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).
• Strong data modeling and data warehouse design skills, transforming 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).
• Comfort using AI-assisted development tools like Codex or Claude Code to move faster, with judgement to validate outputs.
• Clear communication and collaboration with business partners, analysts, engineers, and leadership, translating between technical and business audiences.
• Care about business impact and turning ambiguous finance and revenue questions into reliable, scalable data products and trustworthy metrics.
• Self-starting problem-solver who thinks from first principles, manages priorities across multiple projects, and thrives in a fast-paced, results-driven environment.

Responsibilities

• Design, build, and own scalable data pipelines (Spark/Databricks) ingesting and modeling billing, subscription, payment, and bookings data across the Superhuman Suite.
• Build and maintain foundational datasets for ARR, NRR, bookings, and other revenue metrics with a trusted view of financial performance.
• Contribute to revenue attribution by maintaining underlying datasets that connect product usage, customer lifecycle, and commercial signals into an explainable view.
• Model revenue data into clean, well-documented, reusable tables for Finance, Revenue Ops, analysts, and business partners to self-serve.
• Own data quality, freshness, and reliability for revenue datasets with automated checks, monitoring, alerts, and reconciliation processes.
• Partner with Finance, Revenue Ops, Analytics Eng, Product, and Engineering to translate business questions into robust data models and trustworthy metrics.
• Continuously improve performance, cost efficiency, and developer experience of the finance and revenue data platform.

Skills

SQL

Data modeling

Data warehousing

Data quality

Revenue analytics

Tools

Spark

Databricks

Delta Lake

dbt

Snowflake

Airflow

Git

Codex

Claude Code

Job description

Superhuman is seeking a Data Engineer on the Finance & Revenue team to own revenue data pipelines and datasets across the Grammarly-powered suite. You’ll partner with Finance, Revenue Operations, Analytics, and Engineering to turn billing, bookings, customer, and product-usage signals into reliable, decision-grade data.

This high-ownership role designs scalable pipelines (Spark/Databricks), maintains ARR/NRR datasets, supports the revenue attribution model, and ensures data quality with