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# Data Engineer, Pricing & Comps

- **Company:** McGrath Office Equipment Inc
- **Location:** Jersey City, NJ, United States
- **Experience:** Expert
- **Salary:** $160,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Standard Sql, Apache Spark, Data Pipelines
- **Published:** October 9, 2026
- **Apply:** https://www.disabledperson.com/jobs/75963964-data-engineer-pricing-comps

## About the Role

* 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

## Description

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, * 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