Firmable is the market-leading B2B sales intelligence platform in Asia Pacific —
and we're scaling that success globally at pace. Backed by leading investors and
2,000+ customers strong, we exist to give sales teams an unfair advantage: the
deepest company and people data of any platform, enriched with real-time
signals, served at the right moment by intelligent agents.

Our moat is the data. This role owns whether it can be trusted.

THE ROLE

As Data Engineering Lead — Data Quality Systems, you'll lead a small team of
Applied AI Engineers building the systems that measure, verify, and enforce
quality across billions of company and people records in 13 markets.

This is not a QA or testing role. You won't be writing ETL test cases, app
tests, or product acceptance tests. You'll be architecting the quality layer
itself — verification pipelines, eval harnesses, LLM-based validators, anomaly
detection, and the release gates that decide what data ships to customers.

~80% hands-on engineering. ~20% leading the team. You set the technical bar,
write the hardest code,



and unblock the engineers building on top of your
frameworks. If you want a pure people-management seat, this isn't it.

WHAT YOU'LL OWN

* Quality systems architecture — design and build the verification, sampling,
and scoring pipelines that run continuously over billions of rows across
every market

* Frameworks the team builds on — the harnesses, abstractions, and SKILL.md
specs that make recent quality checks fast to write, cheap to run, and hard to
get wrong

* Harness engineering — eval harnesses for LLM-based validation and extraction:
labelled eval sets, precision/recall tracking, judge calibration, prompt
versioning, drift detection on vendor model updates

* Release gates — pre- and post-production gates that block bad data before it
reaches customers, with the failure analysis and triage tooling to match

* Incident remediation — root-cause quality incidents at scale, ship the fix,
and turn the fail

📌 Data Engineering Lead (India)
🏢 Firmable
📍 India