Data Engineering Lead - Data Quality Systems

**Read this first.

This role is not for you if

**

**Your background is testing or QA**

writing test cases, ETL testing, QA automation, or checking data samples by hand. This is not a testing role, and we won't consider QA profiles

• You've used data quality tools like Great Expectations or Monte Carlo, but never built a quality framework yourself
• You've only worked with tables of thousands of rows, not millions or billions
• You want a people-management seat. This role is 80% hands-on engineering, 20% leading the team
• **You've never had engineers formally reporting to you.** Mentoring or acting as a tech lead without direct reports doesn't count
• You've never led engineers while still writing the hardest code yourself
• You haven't run LLMs in production with eval sets, traces, and cost limits
• Your experience is mostly services or consulting delivery, without long-term ownership of a platform

Firmable sells company and people data to sales teams across Asia Pacific and the US, and we're taking it worldwide. Customers pay for that data being right. This role decides whether it is.

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## What you'll work on

You'll lead 3–5 Applied AI Engineers building the systems that measure and enforce quality across billions of company and people records.

• Verification, sampling and scoring pipelines that run continuously across every market
• LLM and decision-model validators, with labelled eval sets, calibrated judges and drift detection
• Release gates that stop bad data before it reaches a customer
• Root-causing quality incidents and turning each fix into a permanent automated check
• Frameworks and [SKILL.md](http://SKILL.md) specs that make a new check quick to write and cheap to run

## Must haves

• 7–10 years building production data systems that ran unattended
• Eval or test harnesses other engineers depend on. You can show us the repo
• Strong Python and advanced SQL

**Nice to have

** B2B data and entity resolution

• Snowflake, Databricks or Redshift
• AWS
• Airflow
• embeddings for matching
• startup experience
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## How we build

We write code with coding agents like Claude Code by default, and engineers spend their time on design, review and evals. In production we pick the model for the job: frontier LLMs for messy extraction and reasoning, small typed decision models when the answer is a yes/no, a choice or a score, and MCP servers and agent SDKs when a model needs tools. When something new ships, we run it against our evals that week. Every LLM call is logged with its prompt version, model and cost.

Small teams, no layers, weekly releases moving to daily. No fixed hours, no handholding.

## The deal

Competitive base and meaningful equity. The gates, harnesses and validators are mostly unbuilt, so you'll define how quality works here.

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_Firmable is an equal opportunity employer. We believe diverse teams build better products._