Data Engineer, Business Units - Go

Superhuman•San Francisco, CA

2d•$117,000 - $205,000•Hybrid

About The Position

As a Data Engineer on the Business Unit Pod supporting Go, you will build and operate pipelines and data models that help teams understand how people use Superhuman’s proactive AI assistant. You will work with Product, Engineering, and Data Science to provide reliable data for product improvement and impact measurement. Your work will help teams understand user experience across conversations, inline suggestions, and built-in and custom agents, including where users get stuck, which capabilities they value, and what drives continued use. You will also connect product usage to customer adoption and business growth. You will own projects and datasets with a defined scope, supported by experienced engineers. Most of the work will involve production pipelines and reusable data models, with analyses and dashboards as needed to support decisions. The role offers opportunities to take on more complex work as you learn the product and systems. Superhuman offers a dynamic hybrid working model, with preferred Data Platform locations in its San Francisco or Seattle hubs.

Requirements

At least 2 years of experience building and operating production data pipelines, ideally for product analytics or other critical business use cases.
High proficiency in SQL and experience writing, debugging, and optimizing transformations in a modern lakehouse or cloud data warehouse, such as Databricks or Snowflake.
Understanding of data modeling and warehouse design, including the ability to turn complex source data into understandable, reusable tables.
Careful testing practices and the ability to investigate unexpected results and trace data issues to their source.
Experience with workflow orchestration, version control, and deployment practices for data pipelines, using tools such as Databricks Workflows, Airflow, and Git.
Experience using AI-assisted development tools such as Codex, Claude Code, or Cursor, and responsibility for testing and validating generated code.
Clear communication with technical and nontechnical partners, including translating product questions into data requirements and explaining assumptions and limitations.
Curiosity about product usage and how data work can help teams make better decisions.
Ability to follow work through to production, break problems into manageable steps, raise risks and dependencies early, and ask for help when needed.

Nice To Haves

Experience using Python and/or Spark for data transformations, pipelines, or automation.
Experience with product or growth analytics, including adoption funnels, retention, conversion, or A/B experiments.
Experience with event logging and behavioral data from web, desktop, or mobile products.
Experience supporting AI products or agents, including measuring adoption, interaction quality, reliability, latency, or usage costs.
Experience building dashboards or other data tools that product, analytics, and business teams use to answer their own questions.

Responsibilities

Build and maintain production data pipelines in Databricks to process Go product events, user activity, and agent interactions.
Develop datasets for onboarding, activation, engagement, retention, and conversion using consistent metrics.
Partner with Product, Engineering, and Data Science to define measurement needs, improve event logging, and ensure new features are represented correctly in downstream data.
Create understandable data models with clear definitions and documentation that support self-service analytics and AI tools; build focused analyses and dashboards when they help partners use data or make decisions.
Connect Go usage with customer and account data to analyze adoption, customer value, and the product’s contribution to growth and revenue.
Maintain accurate, current, and reliable datasets by adding tests and monitoring, investigating discrepancies, and fixing underlying causes.
Improve pipeline and query performance, manage compute costs, and make code easier to maintain.
Contribute to design decisions and work with engineers and partners across Data Platform and Go.