Senior Data Engineer (Google Cloud / DBT)
The Company
We're building one of the largest networks connecting shoppers to local and specialty retail — the infrastructure that lets people find and buy from real stores near them instead of defaulting to the same three online giants. Since 2014, we've grown into the connective tissue between over 1,000 of the world's best consumer brands, 60,000+ retailers, and the shoppers who buy from them.
The scale is real: 100+ million unique shoppers interact with our products every month, generating over 3 billion engagements a year. Every product view, every store search, every purchase attributed back to a brand — it all flows through the data platform you'd be helping build.
We're a lean, senior team (22 engineers, 52 people total) that punches well above our size. No bureaucracy, no nine layers of approval — just smart people solving a genuinely hard, genuinely large-scale data problem.
The Role
Every shopper interaction on our network lands in our warehouse. Your job is turning that raw firehose into models that some of the biggest retail and consumer brands in the world log into and actually rely on.
You'd own your models end-to-end — not just writing the SQL, but the pipelines underneath, the statistical thinking inside them, and how they get presented to the people using them. This isn't a "ticket-taker" data engineering role. You're expected to take a half-formed question from a salesperson or exec and turn it into something the whole company trusts.
You'll also have a real hand in shaping where we're headed next: AI-powered client-facing products, internal tooling that makes every team faster, and the agentic workflows that are already becoming part of how we build.
What you'll actually do
Own and evolve the dimensional models, data marts, and governance behind both our internal analytics and the client-facing reporting our customers use daily
Build the data models behind our commercial data products — blending solid data engineering with statistical and ML thinking
Help build internal MCP servers, AI-powered chatbots, and automations that put data directly into the hands of the teams that need it
Be the go-to data partner for the rest of the company — the person whose answer to an ad-hoc question turns into the next product feature
What We're Looking For
We're not looking for a converted software engineer who's "kind of interested in data." We're looking for someone who thinks in pipelines, dimensions, and data contracts — someone who's spent real time in the data engineering trenches and is good at it.
Strong SQL at real warehouse scale (we run BigQuery)
Practical data warehousing chops — facts, dimensions, data marts, transformation pipelines. We run Google Dataform today and are migrating to dbt, so experience with either is a big plus. Airflow experience helps too.
Strong Python — pandas or polars, and comfort working directly against cloud APIs
Business acumen — you can sit with a vague ask and turn it into a model people trust, without needing every detail spelled out for you
Clear communication — you document what you build and why, and you can explain a model to someone who will never read your SQL
Bonus points if you have depth in any of:
Retail, e-commerce, or point-of-sale data (and the particular mess that comes with it)
Applied statistical modeling — statsmodels, pymc, regression, time series (ARIMA)
Machine learning in production — sklearn, decision-tree models
LLM/agentic development — MCP, the Anthropic or OpenAI SDKs, evals, fine-tuning
Embedding models for semantic search or ranking
Nobody has all of it. If your fundamentals are strong and you're genuinely curious about the rest, we want to talk.
Why This Role
100% remote, no travel, no in-person requirement — work from wherever you do your best work
Real ownership — your models, your pipelines, your name on the product
Scale that matters — you're not optimizing a toy dataset, you're powering decisions for 1,000+ real brands
A team that's building the future of the role — you'll help shape how AI gets used internally and in client-facing products, not just watch it happen elsewhere
A senior, tight-knit engineering culture that values curiosity, craftsmanship, and getting things right over corporate process