founding data pipeline engineer

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.