Growth Data Engineer: Scalable Pipelines & ML

Superhuman; San Francisco (CA); Hybrid; USD 190,000 - 240,000; Full time

Benefits offered by this job

Excellent health care

401(k) matching

Paid parental leave

20 days PTO + holidays

Professional development budget

Job summary

Superhuman is seeking a Data Engineer on the RTM Growth team to own pipelines, models, and datasets powering user acquisition across our AI-native productivity platform. You’ll partner with Growth, Performance Marketing, and Data Science to turn signals into reliable data.

This role blends data engineering with ML readiness in a fast-paced environment. Great candidates will have 3+ years building production data pipelines, strong SQL/Python skills, and experience with Spark, lakehouse, and cloud

Qualifications

• 3+ years of experience building and operating production data pipelines and data platforms.
• Proficient in SQL and Python with hands-on Spark experience.
• Strong data-modeling and warehouse-design skills with data quality focus.
• Experience with CI/CD and workflow tools for data (Airflow or Databricks Workflows).
• Ability to translate business questions into robust data models.

Responsibilities

• Design, build, and own scalable data pipelines powering growth analytics.
• Develop feature and training datasets for ML models and productionize them with Data Science.
• Create measurement, attribution, and experimentation data layers for web/landing pages.
• Model growth data into reusable tables for self-serve access by analysts.
• Ensure data quality, freshness, and reliability with monitoring and alerts.
• Collaborate with Growth, Marketing, Analytics Engineering, and Data Science.

Skills

SQL

Python

Spark

Databricks

Data modeling

Airflow

Tools

Databricks

Delta Lake

dbt

Snowflake

Job description

Superhuman is seeking a Data Engineer on the RTM Growth team to own pipelines, models, and datasets powering user acquisition across our AI-native productivity platform. You’ll partner with Growth, Performance Marketing, and Data Science to turn signals into reliable data.

This role blends data engineering with ML readiness in a fast-paced environment. Great candidates will have 3+ years building production data pipelines, strong SQL/Python skills, and experience with Spark, lakehouse, and cloud