Founding Data Pipeline Engineer

1. building catalog data pipelines

• design and maintain pipelines that ingest, normalize, enrich, and version product/catalog data
• define schemas that age well as the product evolves
• handle messy, incomplete, and inconsistent data without hiding the mess
• make catalog data usable for downstream systems (search, recommendations, personalization)

2. owning data clarity end-to-end

• decide what should be logged and what should not
• ensure every dataset has a clear purpose and owner
• detect and debug silent failures, drift, and data pollution
• make pipelines observable, debuggable, and boring in the best way

3. making decisions irreversible

• build systems that allow the team to confidently:
  • trust metrics
  • kill features
  • iterate without second-guessing the data
• reduce ambiguity for product and machine learning decisions, not add to it

4. setting engineering standards early

• establish patterns for data hygiene, versioning, and validation
• write documentation that explains why something exists, not just how
  • push back on over-engineering and under-thinking equally