> Markdown version of [/jobs/ext/4636519-founding-data-engineer-pricing-and-comps](https://www.wearedevelopers.com/jobs/ext/4636519-founding-data-engineer-pricing-and-comps). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Founding Data Engineer, Pricing and Comps

- **Company:** ONE STOP COLLECTIBLE CORP
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $160,000 - $220,000
- **Contract:** Permanent contract
- **Skills:** Airflow, Computer Vision, Information Engineering, Extract Transform Load (ETL), Database Queries, Apache Spark, Marketplace, Data Pipelines
- **Published:** November 8, 2026
- **Apply:** https://startup.jobs/founding-data-engineer-pricing-and-comps-clera-10714809

## About the Role

* At least 5 years of data engineering experience, including building and owning large-scale production ETL and data pipelines.
 * Experience working with messy, adversarial data in marketplace, pricing, fraud, or similarly noisy domains.
 * Strong SQL skills and experience with modern data tools such as dbt, Airflow or Dagster, and Spark or similar technologies.
 * Experience with statistical estimation or pricing models, including basic regression-based comparable-sales logic.
 * Experience designing entity resolution or record-linkage systems at scale, and production monitoring for data quality, drift, or anomalies.
 * Experience processing data from multiple external sources, making technical build-versus-buy decisions, and communicating pricing confidence and uncertainty to non-technical stakeholders.
 * Pricing intelligence, third-party data licensing, or early-stage startup experience is a plus.

## Description

As the founding data engineer on a marketplace team, you will build the data pipelines and pricing systems that turn inconsistent sold-listing data into reliable comparable sales and estimates. Your work will help ensure users and stakeholders can understand both the price and its confidence., * Build and own scheduled ingestion pipelines for sold-listing data from multiple marketplaces and external sources.

 * Design entity resolution systems that match inconsistent listing text and images to canonical items.
 * Develop valuation logic that weighs comparable sales by recency, condition, and the number of available comps.
 * Monitor data quality to identify drift, fraud, stale comps, and other integrity issues before they affect users.
 * Partner with computer vision colleagues to define item matching across sources with differing levels of detail.
 * Make build-versus-buy decisions about data sources, licensing, and third-party pricing feeds.