Data Engineer - Pricing & Comps (Equity)

Moe; New York (NY); On-site; USD 160,000 - 220,000; Full time

Benefits offered by this job

Equity

Job summary

Moe is building the pipelines to turn messy sold-listing data into trusted comps. You will own ingestion pipelines pulling data from multiple marketplaces at scale, and design entity resolution that maps noisy listings to canonical items.

You will develop pricing models that weight recency and condition, implement data quality monitoring to catch drift and fraud, and collaborate with CV lead to harmonize the notion of the same item across sources.

Qualifications

• 5+ years of data engineering experience building and owning large-scale ETL/data pipelines in production.
• Experience with messy, adversarial, real-world data in marketplaces, pricing, fraud or similarly noisy domains.
• Strong SQL and a modern data stack (dbt, Airflow or Dagster, Spark or similar).
• Exposure to statistical estimation or pricing models; basic regression-based comps logic counts.

Responsibilities

• 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 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.
• 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.

Skills

Data engineering

SQL

Data pipelines

Data quality

Communication

Pricing concepts

Tools

dbt

Airflow

Dagster

Spark

Job description

Moe is building the pipelines to turn messy sold-listing data into trusted comps. You will own ingestion pipelines pulling data from multiple marketplaces at scale, and design entity resolution that maps noisy listings to canonical items.

You will develop pricing models that weight recency and condition, implement data quality monitoring to catch drift and fraud, and collaborate with CV lead to harmonize the notion of the same item across sources.