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Salary: USD 190,000 - 240,000 / annual
Superhuman (formerly Grammarly, now part of the Superhuman suite) is seeking a Data Engineer to join the RTM Growth team in a hybrid role based in San Francisco or Seattle. You will own the data pipelines, models, and datasets that power user acquisition and growth across Superhuman's multi-product AI platform, which includes Grammarly's writing assistance, Mail, Docs, Databases, and Go.
In this high-ownership role, you'll design and build scalable data pipelines using Spark and Databricks that power ad bidding and paid acquisition optimization across Google, Meta, and LinkedIn. You'll develop feature and training datasets for machine learning models focused on bid optimization, budget allocation, and audience targeting, working closely with Data Science to productionize these models. You'll also build the measurement, attribution, and experimentation data layer that enables Growth to trust web and landing-page optimization results.
Your responsibilities include modeling growth and marketing data into clean, well-documented, reusable tables for self-service by analysts and data scientists; owning data quality, freshness, and reliability through automated checks and monitoring; and partnering with Growth, Marketing, Analytics Engineering, and Data Science teams to translate business questions into robust data models. You'll continuously optimize the performance, cost efficiency, and developer experience of the growth data platform.
Superhuman operates as a compound startup building integrated products rather than standalone tools, creating an unusually rich data opportunity spanning paid acquisition, self-serve funnels, and cross-product usage for both consumers and enterprises. Your work will directly influence how efficiently the company invests in growth and shapes the next wave of user acquisition.
QUALIFICATIONS:
- 3+ years of experience building and operating production data pipelines and data platforms, ideally for growth, marketing, or experimentation use cases
- Highly proficient in SQL and Python with deep hands-on experience in Spark and a modern lakehouse or cloud data warehouse (Databricks, Delta Lake, dbt, Snowflake, or similar)
- End-to-end support of machine learning workflows, including building feature pipelines, serving training and inference datasets, and partnering with data scientists to move models into production
- Strong data-modeling and warehouse-design skills with a rigorous approach to data quality and observability
- 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 Claude Code or Codex with good judgment to validate and supervise their output
- Clear communication and collaboration skills across Growth, Marketing, and Data Science teams
- Business-impact orientation and ability to turn ambiguous growth questions into reliable, scalable data products
- Self-starting problem-solver who thinks from first principles, manages multiple priorities, and thrives in fast-paced, results-driven environments
NICE TO HAVE:
- Exposure to growth and performance-marketing domains, especially ad bidding, paid-acquisition optimization, and web/landing-page experimentation
- Comfort with marketing and ad-platform data (Google Ads, Meta, LinkedIn) and attribution/measurement concepts
- Track record of building self-serve data products that other teams rely on
- Experience partnering with performance marketers or growth leaders as a strategic data partner
