Founding Data Engineer (Pricing/Comps)
New
J
JobgetherOnline marketplace pricing
Based in CanadaFull-TimeMiddle
Salary160,000 - 220,000 USD per year
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Job Details
- Experience
- At least five years of data engineering experience
- Required Skills
- SQLETLAirflowData engineeringSparkdbt
Requirements
- Have at least five years of data engineering experience.
- Have owned large-scale, production ETL processes and data pipelines.
- Have experience handling messy, inconsistent, or adversarial data in marketplaces, pricing, fraud detection, or similarly complex data environments.
- Have strong SQL skills.
- Have hands-on experience with modern data engineering tools such as dbt, Airflow, Dagster, Spark, or comparable technologies.
- Have experience with statistical estimation and pricing models, including regression-based approaches to identifying comparable items and estimating value.
- Have demonstrated experience with entity resolution and record linkage at scale.
- Have experience with production data quality monitoring.
- Be able to make sound decisions when data is incomplete, ambiguous, or unreliable.
- Be able to explain pricing confidence, uncertainty, and technical trade-offs to non-technical stakeholders.
Responsibilities
- Build, maintain, and own scheduled data ingestion pipelines for sold-listing information from marketplaces and external sources.
- Design and implement entity resolution systems that match inconsistent listing descriptions, text, and images to canonical items.
- Develop pricing and valuation logic that accounts for listing recency, item condition, and comparable sales.
- Establish production-grade data quality monitoring to identify data drift, fraud, anomalies, stale listings, and unreliable information.
- Partner with the computer vision lead to define item-matching standards across sources with varying detail and data quality.
- Evaluate build-versus-buy decisions for data sources, licensing agreements, and third-party pricing feeds.
- Communicate pricing confidence, data limitations, and uncertainty to non-technical stakeholders.
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