Senior Engineer - Product Data

Overview

Build the data backbone for AI applications used by millions. You'll design pipelines that ingest messy enterprise data, transform it into clean datasets, and serve it through performant APIs. Work spans the full stack: database schema design, ETL pipelines, API development, query optimization. You'll make architectural decisions that directly impact product performance and reliability.

Responsibilities

•Design and implement data pipelines for client AI applications
•Build APIs that serve ML models and analytics dashboards
•Optimize database queries and schema for scale
•Debug data quality issues and implement validation logic
•Monitor system performance and respond to incidents
•Code review and mentor junior engineers

Requirements

•5+ years building production data systems (pipelines, warehouses, APIs)
•Strong proficiency in Python and SQL
•Experience with distributed systems and data modeling
•Track record shipping features to production used by real users

Required Skills

PythonSQLPostgreSQLData Modeling

Preferred Qualifications

•Built systems processing millions of events per day
•Experience with modern data stack (dbt, Airflow, Spark)
•Worked in fast-growing startups or high-performance teams
•Contributions to open-source data projects

Nice to Have

DockerAWS

A Day in the Life

Morning: Debug slow query in client dashboard. Mid-morning: Design review for new pipeline architecture. Afternoon: Implement incremental ETL for real-time feature. Late afternoon: Code review and pair programming session.

Why This Role

Work on systems that matter. See your code run in production at Fortune 500 companies. Learn from senior engineers who've built infrastructure at scale. Ship fast without bureaucracy.

Technical Challenge

This role requires completing a technical challenge as part of the application process. Challenge: Medium: Analytics Dashboard Backend

View Challenge Details

Frequently Asked Questions

What's the tech stack?

Primary: Python, PostgreSQL, Docker, AWS. We adapt to client constraints but generally push for modern tooling. You'll have input on technology decisions.

How much client interaction?

More than typical engineering roles. You'll join architecture discussions and occasionally present technical designs to client teams.

What about technical challenge?

Required. You'll complete a medium-difficulty data pipeline challenge (6-8 hours). We evaluate: code quality, system design, performance considerations.