Founding Data Pipeline Engineer

HuntingCube; Gurugram District; On-site; INR 1,200,000 - 2,500,000; Full time

Job summary

HuntingCube is building consumer AI with a focus on clean, trustworthy data for catalog and product data pipelines. This role ensures data pipelines are robust, observable, and easy to question, emphasizing correctness over speed.

You will design schemas, enforce data contracts, and own end-to-end data clarity, enabling reliable search, recommendations, and personalization.

Qualifications

• Experience building catalog data pipelines and ETL/ELT processes.
• Ability to define data schemas and contracts for downstream systems.
• Track record of debugging data quality issues and data drift.

Responsibilities

• Build catalog data pipelines to ingest, normalize, enrich, and version data.
• Own data clarity end-to-end by logging decisions and ownership.
• Make data pipelines observable, debuggable, and boring in the best way.
• Set engineering standards for hygiene, versioning, and validation and document rationale.

Skills

Data pipelines

Data quality

Schemas

ETL/ELT

Debugging

Observability

Documentation

Versioning

Data contracts

System design

Job description

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. 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. 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. 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. 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