About this opportunity

Moe lists this Data Engineer, Pricing & Comps opportunity in new york, New York. Review the employer’s description below for duties, qualifications and application requirements.

Job description

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.

To be considered for an interview, please make sure your application is full in line with the job specs as found below.

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 xhqgsiq startup experience

What success looks like

30 days: fully ramped on available data sources; has ingested and profiled data for the first category

60 days: working comps pipeline live for one category — ingest, dedupe, match, price — with a visible confidence range

90 days: pipeline extended to 2–3 categories, with data quality monitoring catching bad comps automatically

Process

Intro 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)