Job Description

About the job team: product (fashion, taste, personalization) why this role exists polopan is building consumer ai where taste, context, and judgment matter more than raw scale. our models are only as good as the truthfulness of the data beneath them. this role exists to make sure our catalog and product data pipelines are: clean explainable trustworthy hard to lie to we care less about how fast things move, and more about whether they ever need to be questioned again. what you'll be responsible for 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 what we care about (more than speed) we don't measure this role by: number of tickets closed lines of code written how fast you ship we measure it by: how much confusion disappears after your work exists how rarely your systems need revisiting how confidently others can build on top of what you've built sometimes deadlines will exist — not to rush you, but to force clarity on what truly matters. what we're looking for you'll likely resonate if you: enjoy turning messy reality into clean, minimal systems think deeply about schemas, contracts, and downstream consequences prefer deleting data to hoarding it care about correctness, not cleverness are calm under constraint and decisive under deadlines experience that helps (not all required): building data pipelines (etl / elt) in production environments working with catalog, marketplace, or content-heavy datasets designing event schemas and data contracts debugging data quality issues that don't throw errors familiarity with batch + near-real-time systems tech stack specifics matter less than your judgment. python would be nice to have. what this role is not not a ship fast, break things role not a model-training or research-heavy ML role not a growth or analytics-only role this is a foundational engineering role. What you build early will shape everything that comes after. how success looks (first 90 days) we trust our catalog data without caveats product and ML teams stop asking is this data right at least one major product decision becomes irreversible because of your work parts of the system become confidently deletable if that sounds like a good problem to work on, we'd like to talk. final note we're building this company deliberately. if you care more about clarity than velocity, and about doing things once, properly, you'll feel at home here.

Required Skills

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Additional Information

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Founding Data Pipeline Engineer