Data Engineer

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.

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 proactively delivers help.

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  and about our values here.

As a Data Engineer on the RTM Growth team, you’ll own the pipelines, models, and datasets that power how Superhuman acquires and grows users across our multi-product, AI-native productivity platform:
Grammarly’s writing assistance, Mail, Docs, Databases, and Go. You’ll partner closely with Growth, Performance Marketing, and Data Science to turn raw acquisition, ad-platform, 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 data opportunity, with signals spanning paid acquisition, self-serve funnels, and cross-product usage for both consumers and enterprises. The data you model connects those surfaces, and the systems you build directly shape how efficiently we invest in growth and where our next wave of users comes from.

This is a high-ownership, high-impact role at the intersection of data engineering, machine learning, and growth. You’ll own growth-critical systems end-to-end, and your work will move the metrics the whole company watches.

• Design, build, and own scalable data pipelines (Spark/Databricks) that power ad bidding and paid acquisition optimization across channels such as Google, Meta, and Linked In.
• Build and maintain the feature and training datasets that machine learning models rely on for bid optimization, budget allocation, and audience targeting, and help product ionize those models alongside Data Science.
• Develop the measurement, attribution, and experimentation data layer behind web and landing-page optimization, so Growth can trust the numbers behind every test.
• Model growth and marketing data into clean, well-documented, reusable tables that analysts and data scientists can self-serve from.
• Own data quality, freshness, and reliability for growth-critical datasets, with automated checks, monitoring, and alerting.
• Partner with Growth, Marketing, Analytics Engineering, and Data Science to translate business questions into robust data models and trustworthy metrics.
• Continuously improve the performance, cost efficiency, and developer experience of our growth data platform.

You’ll do this alongside partners across Growth, Performance Marketing, Analytics Engineering, and Data Science. 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.

• You have 3+ years of experience building and operating production data pipelines and data platforms, ideally for growth, marketing, or experimentation use cases.
• You’re 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).
• You’ve supported machine learning workflows end-to-end, building feature pipelines, serving training…