Skip to main content

Broccoli AI

Founding Data Engineer

  • Confirmed live in the last 24 hours
  • $180k–$230k
  • Mid level
  • Full-time
  • On-site · San Francisco
  • 4+ yrs exp
  • Added 1 month ago

About this role

Broccoli seeks a founding data analytics engineer to build and own the company's unified data layer. You'll design production pipelines, create clean data models, and establish a source-of-truth library that powers customer dashboards, internal analytics, and AI-ready datasets across the organization.

What you'll do

  • Design and operate reliable data pipelines ingesting from multiple sources into ClickHouse
  • Model raw data into clean, documented tables with entity resolution across systems
  • Build canonical metric definitions and source-of-truth views for company-wide use
  • Implement data quality checks, freshness monitoring, and alerting for production reliability
  • Conduct ad-hoc analyses and deep dives to support strategy and operations decisions
  • Collaborate with engineering on data architecture and schema design for warehouse usability

What they're looking for

  • SQL and Python
  • ETL tooling (Airbyte, Fivetran, Dagster, dbt, or custom)
  • ClickHouse or similar OLAP/columnar warehouse
  • Data modeling and schema design
  • Data pipeline orchestration
  • Entity resolution and multi-source data integration
  • Data quality and testing frameworks
  • Analytics and metric definition
Apply with Autofill

Opens the application — the Jobs AI extension fills it for you. Set up autofill

Opens the official application on the employer’s site. No login required.

Broccoli AI

Broccoli AI builds AI-powered operating systems that automate front-office operations for home service contractors, handling workflows like job booking, customer follow-ups, and dispatch. The company is hiring Software Engineers to develop core product features and growth infrastructure that drive customer acquisition and retention.

Industry
Technology & Software
View all jobs at Broccoli AI

Likely interview questions

  • Walk us through a time you built a production data pipeline from scratch—what sources did you integrate, and how did you ensure data quality?
  • How would you approach entity resolution if customer data arrives fragmented across billing, support, and call systems?
Unlock all 7 questions free — and practice them live →