Closing on: Sep 30, 2026

Job Category: Engineering

Job Type: Full Time

Job Location: Gurgaon 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
- 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)
- owning data clarity end-to-end
- decide what should be logged and what should not
- ensure every dataset has a explicit purpose and owner
- detect and debug silent failures, drift, and data pollution
- make pipelines observable, debuggable, and boring in the best way
- 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
- 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.

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 quick things move, and more about whether they ever need to be questioned again. what you’ll be responsible for
- 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)
- 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
- 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
- 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

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.

📌 founding data pipeline engineer (Gurugram)
🏢 polopan
📍 Gurugram