Growth Data Engineer: Build Scalable Data Pipelines

Superhuman; Seattle (WA); Hybrid; USD 190,000 - 240,000; Full time

Benefits offered by this job

Excellent health care

Disability and life insurance

401(k) matching

Paid parental leave

Paid time off

Professional development budget

Job summary

Superhuman is seeking a Data Engineer on the RTM Growth team to own scalable data pipelines and datasets powering user acquisition and growth across our AI-native productivity suite. You will partner with Growth, Performance Marketing, and Data Science to turn raw signals into reliable metrics and data products.

This hybrid role offers high ownership, opportunities to impact cross-product data, and a fast-paced environment focused on scalable data platforms and experimentation.

Qualifications

• 3+ years building and operating production data pipelines for growth/experimentation use cases.
• Proficient in SQL and Python with hands-on Spark experience.
• Experience with modern lakehouse/cloud data warehouses and data quality observability.
• Familiarity with ML workflows and productionizing datasets.

Responsibilities

• Design, build, and own scalable data pipelines powering growth and acquisition.
• Develop feature and training datasets for ML models and productionize them with Data Science.
• Create measurement, attribution, and experimentation data layers for web/landing tests.
• Model growth data into clean, reusable tables for analysts.
• Own data quality, freshness, and reliability with monitoring and alerts.
• Collaborate with Growth, Marketing, Analytics Engineering, and Data Science to translate questions into robust data models.

Skills

SQL

Python

Data modeling

Communication

Tools

Spark

Databricks

Delta Lake

dbt

Snowflake

Job description

Superhuman is seeking a Data Engineer on the RTM Growth team to own scalable data pipelines and datasets powering user acquisition and growth across our AI-native productivity suite. You will partner with Growth, Performance Marketing, and Data Science to turn raw signals into reliable metrics and data products.

This hybrid role offers high ownership, opportunities to impact cross-product data, and a fast-paced environment focused on scalable data platforms and experimentation.