About the role:
Moe's price is only as good as the comps behind it, and real-world sold-listing data is messy by default — duplicate listings, wrong categories, faked or manipulated prices, inconsistent condition grading. You'll build the pipeline that turns that mess into comps a user can trust. This is arguably the least visible and most decision-critical part of the product - so a crucial role for us.
What you'll do:- Build and own ingestion pipelines pulling sold-listing data from multiple marketplaces and sources, at scale and on a schedule
- Design entity resolution logic that matches messy, inconsistent listing data (text + images) back to a canonical item
- Build the valuation logic that turns a set of matched comps into a price estimate — weighting recency, condition, and comp count appropriately
- Build data quality monitoring that catches drift, fraud, and stale comps before they reach a user
- Partner with the CV lead on what "the same item" means across data sources with different levels of detail
- Make build-vs-buy calls on data sources, licensing deals, and third-party pricing feeds
What we're looking for:- 5+ years in data engineering, with direct experience building and owning large-scale ETL/data pipelines in production
- Real experience with messy, adversarial, real-world data — marketplace, pricing, fraud, or a similarly noisy domain
- Strong SQL and a modern data stack (dbt, Airflow or Dagster, Spark or similar)
- Some exposure to statistical estimation or pricing models — even basic regression-based comps logic counts
- Comfortable owning ambiguous data quality tradeoffs with no perfect answer
- Strong communication — you'll need to explain confidence and uncertainty in pricing to non-technical stakeholders
Nice to have:- Background in pricing intelligence — real estate AVMs (Zillow Zestimate), used-car pricing (Carvana, CarGurus), or resale marketplaces
- Experience with entity resolution / record linkage at scale
- Experience negotiating or managing third-party data licensing relationships
- Founding or early-stage startup experience
What success looks like30 days: fully ramped on available data sources; has ingested and profiled data for the first category60 days: working comps pipeline live for one category — ingest, dedupe, match, price — with a visible confidence range90 days: pipeline extended to 2–3 categories, with data quality monitoring catching bad comps automatically
ProcessIntro call → technical deep dive on a past pipeline you've built → a scoped take-home or pairing session on a real Moe data-matching problem → founder conversation → offer
Comp: $160K–$220K base + 0.5%–1.0% equity (negotiable for the right hire)
