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As a Data Engineer on the RTM Growth team at Superhuman (formerly Grammarly), you'll own the data pipelines, models, and datasets powering user acquisition and growth across a multi-product AI-native platform including Grammarly, Mail, Docs, Databases, and Go. You'll partner closely with Growth, Performance Marketing, and Data Science teams to transform raw acquisition, ad-platform, and product-usage signals into reliable, decision-grade data that directly shapes how the company invests in growth.
Key responsibilities include designing and building scalable data pipelines using Spark and Databricks for ad bidding and paid acquisition optimization across Google, Meta, and LinkedIn. You'll build and maintain feature and training datasets for machine learning models focused on bid optimization, budget allocation, and audience targeting. You'll develop the measurement, attribution, and experimentation data layer supporting web and landing-page optimization, ensuring Growth can trust test results. Your work includes modeling growth and marketing data into clean, well-documented, reusable tables for self-serve analytics, owning data quality and freshness with automated monitoring and alerting, and continuously improving platform performance and cost efficiency.
You'll have high ownership of growth-critical systems end-to-end, with your work directly moving company-wide metrics. The role sits at the intersection of data engineering, machine learning, and growth strategy within a compound startup model that creates unusually rich data opportunities spanning paid acquisition, self-serve funnels, and cross-product usage signals.
Required qualifications: 3+ years building and operating production data pipelines for growth, marketing, or experimentation; strong SQL and Python proficiency with deep Spark experience; hands-on work with modern lakehouses or cloud data warehouses (Databricks, Delta Lake, dbt, Snowflake); end-to-end ML workflow support including feature pipelines and model productionization; strong data modeling and warehouse design with rigorous data quality practices; workflow orchestration and CI/CD experience (Databricks Workflows, Airflow); comfort with AI-assisted development tools; clear communication and cross-functional collaboration skills.
Nice-to-have: growth and performance-marketing domain exposure, ad bidding and paid-acquisition optimization experience, marketing and ad-platform data familiarity, track record building self-serve data products, strategic partnership experience with performance marketers.
