Website:
polopan.com
Job details:
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 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
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 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 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.
Click on Apply to know more.
