Data Analytics Engineer

About Broccoli

At Broccoli, we're building AI teammates for the people who build and maintain our world.

We partner with plumbing, HVAC, and electrical contractors, the hardworking businesses that keep homes and communities running, and replace fragmented software and repetitive manual work with AI that actually gets the job done.

Our AI teammates answer phones, book appointments, follow up with customers, recover missed revenue, and help contractors deliver exceptional customer experiences, all while integrating seamlessly with platforms like ServiceTitan.

Before we wrote a single line of code, we visited more than 140 contractors to understand how these businesses truly operate. That firsthand experience shaped everything we've built.

Today:

• Hundreds of contractors rely on Broccoli every day.

• We've grown to $15M+ ARR in under two years.

• We're trusted by everyone from independent contractors to some of the largest private equity backed operators in the home services industry.

• We're backed by Khosla Ventures and Y Combinator.

We're still just getting started.

About the role

Our data is rich and comes from many sources. Every customer dashboard, every business review, every metric the company runs on draws from it — and we haven't yet built the unified data layer that makes all of that fast, consistent, and ready to scale.

You'll build that layer and own it. You'll model our data into clean, documented tables and build the source-of-truth library and own the data definitions the whole team runs on. You'll work hand in hand with the Strategy & Ops team — and essentially every tool we build, especially the external-facing dashboards and analytics our customers see, will be built on your work.

You're the team's first dedicated data hire: you own the architecture, the tooling choices, and the trust in every number. What you build powers customer-facing dashboards, cross-customer benchmarks, and eventually the business intelligence we ship inside the product.

What you'll do

• Build and run the pipelines. Reliable ingestion from all our sources into ClickHouse — you choose the tooling and own the flow.

• Model the data. Turn raw feeds into clean, documented tables — including entity resolution, so a customer is the same customer across billing, support, and call data.

• Build the source-of-truth library. Canonical views and metric definitions that every dashboard and analysis reads from.

• Make the data Human & AI-ready. Structure our models, definitions, and documentation so both people and AI agents can query them and get the right answer — then build the internal tools that let anyone at Broccoli ask a data question and trust the response.

• Keep it trustworthy. Freshness checks, quality tests, and alerts — we find out a pipeline broke before a customer does.

• Run deep dives when the team needs them. Ad-hoc analyses, segment investigations, partner questions.

• Work closely with engineering. Understand how our systems store and produce data, including schemas, events, and architecture, and give input early on changes so the data that lands in the warehouse is usable, stable, and easy to model.

What we're looking for

• 4–8+ years in data or analytics engineering — you've built and operated production pipelines end to end, and been the one paged when they broke.

• Strong SQL and solid Python; hands-on with ETL tooling (Airbyte, Fivetran, Dagster, dbt, or hand-rolled) and orchestration.

• Real experience with a columnar/OLAP warehouse — ClickHouse ideally; BigQuery, Snowflake, or Redshift transfer fine.

• Data modeling as a craft: you've designed the tables other people query, and you care what the numbers mean, not just that the pipes run.

Nice to have

• Self-directed: you've been the first or only data person somewhere, or built a data platform from scratch

• ClickHouse specifically — materialized views, performance tuning on event-scale data.

• Multi-source identity / entity resolution experience.

• Exposure to customer-facing or multi-tenant analytics (strict customer-level data isolation).

• B2B SaaS operational data — calls, bookings, jobs, billing — or CRM/field-service data like ServiceTitan.