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Superhuman (formerly Grammarly, now part of the Superhuman platform) is seeking a Data Engineer to join the Business Units Pod supporting Go, their proactive AI assistant. You'll build production data pipelines and models that help the company understand how users interact with Go's conversational interface, inline suggestions, and custom agents.
In this role, you'll own projects with clear scope, working closely with Product, Engineering, and Data Science teams. Your primary focus will be building production pipelines in Databricks that process product events, user activity, and agent interactions. You'll develop datasets for key metrics like onboarding, activation, engagement, retention, and conversion—creating consistent definitions that enable self-service analytics across teams.
Key responsibilities include:
- Building and maintaining production data pipelines in Databricks for product events and user activity
- Developing reusable data models with clear documentation to support self-service analytics
- Collaborating with Product, Engineering, and Data Science to define measurement requirements and validate feature representation in downstream data
- Connecting Go usage with customer and account data to measure adoption, customer value, and revenue impact
- Ensuring data accuracy, reliability, and performance through testing, monitoring, and cost optimization
- Contributing focused analyses and dashboards when they support decision-making
You'll work in a hybrid model with preferred locations in San Francisco or Seattle hubs. The team values understanding problems deeply, questioning assumptions, and following through on complex investigations. As you grow your expertise, you'll take on increasingly complex projects.
QUALIFICATIONS:
- 2+ years of experience building and operating production data pipelines, ideally for product analytics or critical business use cases
- High proficiency in SQL with experience writing, debugging, and optimizing transformations in modern lakehouse or cloud data warehouse platforms (Databricks, Snowflake)
- Understanding of data modeling and warehouse design; ability to transform complex source data into reusable tables
- Careful testing practices; ability to investigate unexpected results and trace issues to root cause
- Experience with workflow orchestration, version control, and deployment practices (Databricks Workflows, Airflow, Git)
- Familiarity with AI-assisted development tools (Codex, Claude Code, Cursor) and responsibility for validating their output
- Clear communication with technical and nontechnical partners; ability to translate product questions into data requirements
- Curiosity about product usage and how data work supports better decision-making
- Ability to follow work through to production, break problems into manageable steps, and raise risks early
NICE TO HAVE:
- Python and/or Spark experience for data transformations and automation
- Product or growth analytics experience (adoption funnels, retention, conversion, A/B testing)
- Event logging and behavioral data experience from web, desktop, or mobile products
- Experience supporting AI products or agents (measuring adoption, interaction quality, reliability, latency, costs)
- Dashboard or data tool building for product, analytics, and business teams
