Own the architecture, ingestion, transformation, and production operations of a multi-source data platform, delivering reliable company intelligence through event-driven processing and orchestrated workflows.
Founding Data Engineer (Pricing/Comps)
π Description
- Build and own scheduled ingestion pipelines for sold-listing data from marketplaces and external sources.
- Link inconsistent listing text and images to canonical items through entity resolution.
- Develop comparable-sales valuation logic that accounts for recency, condition, and the number of comps.
- Monitor production data for drift, fraud, stale comparisons, and other quality issues.
- Evaluate build-versus-buy options for data sources, licensing, and pricing feeds.
π οΈ Requirements
- At least 5 years of data engineering experience, including ownership of large-scale production ETL and data pipelines.
- Experience working with messy, adversarial data in marketplaces, pricing, fraud, or a similarly noisy domain.
- Strong SQL skills and experience with modern data tools such as dbt, Airflow or Dagster, and Spark or similar technologies.
- Experience with entity resolution or record linkage at scale, and production data quality monitoring.
- Exposure to statistical estimation or pricing models, including regression-based comparable-sales logic.
- Experience ingesting data from multiple external sources and making technical build-versus-buy decisions.
- Pricing intelligence, data licensing, and early-stage startup experience are helpful.
β¨ Benefits
- Base salary: $160,000 to $220,000 annually. Visa sponsorship is not available.
- Partner with computer vision teammates to define item matching across sources.
Full job description
About the Role
Join an early-stage internet marketplace company to build the data systems behind reliable pricing estimates. You will turn noisy sold-listing data into trustworthy comparable items and pricing signals, working closely with computer vision partners.
What You'll Do
Build and own scheduled ingestion pipelines for sold-listing data from multiple marketplaces and external sources.
Develop entity resolution that links inconsistent listing text and images to canonical items.
Create valuation logic that estimates prices from comparable sales, accounting for recency, condition, and the number of comps.
Build production monitoring to identify data drift, fraud, stale comparisons, and other quality issues.
Partner with computer vision teammates to define item matching across sources with varying levels of detail.
Evaluate build-versus-buy options for data sources, licensing, and third-party pricing feeds.
What We're Looking For
At least 5 years of data engineering experience, including ownership of large-scale production ETL and data pipelines.
Experience working with messy, adversarial data in marketplaces, pricing, fraud, or a similarly noisy domain.
Strong SQL skills and experience with modern data tools such as dbt, Airflow or Dagster, and Spark or similar technologies.
Experience with entity resolution or record linkage at scale, and production data quality monitoring.
Exposure to statistical estimation or pricing models, including regression-based comparable-sales logic.
Experience ingesting data from multiple external sources and making technical build-versus-buy decisions.
Pricing intelligence, data licensing, and early-stage startup experience are helpful.
Compensation & Benefits
Base salary: $160,000 to $220,000 annually. Visa sponsorship is not available.
Location
Hybrid role based in New York, New York.
Similar jobs
Search more Data Engineer jobsOwn the data platform end to end, designing and operating ingestion, event-driven processing, orchestration, and transformation systems that deliver reliable, current data to downstream products.
Senior Data Engineer 2026 -COL
As a Senior Data Engineer, you will design and develop analytical solutions, build data pipelines, and collaborate with clients to improve their data-driven insights.
Senior Data Engineer 2026 - US
As a Senior Data Engineer, you will design and implement AI-optimized data platforms and collaborate with clients to solve complex data engineering challenges across various industries.
Architect, build, and operate scalable data infrastructure for research and trading, integrating market data, news, and other sources while ensuring data quality, performance, security, and cost efficiency.
