Data Engineer - Data Platform

What We're Looking For</h2><p><strong>Must Haves</strong></p><ul><li><p><strong>3+ years</strong> in data engineering or data infrastructure, with production pipelines you've owned end to end and 2+ years experience with Snowflake</p></li><li><p><strong>Strong Python and SQL</strong> — production-grade, performance-aware, comfortable at very large scale</p></li><li><p><strong>Shipped LLMs inside data pipelines</strong> — extraction, enrichment, or validation in production, with structured outputs and a labelled eval set that tells you where the model gets it wrong</p></li><li><p><strong>Harness engineering experience</strong> — you've built or maintained eval or test harnesses for LLM outputs, and you can talk through what they caught</p></li><li><p><strong>Sharp judgement on rules vs. LLMs</strong> — you reach for a regex or a dbt test first and can defend the call either way</p></li><li><p><strong>Production dbt and Airflow</strong> — modular, tested models; DAGs that recover gracefully</p></li><li><p><strong>Cloud warehouse experience</strong> — Snowflake preferred; schema design, query optimisation, cost management</p></li><li><p><strong>AI coding tools are how you work</strong> — Claude Code, Cursor, or equivalent, daily, with real shipped work to show for it</p></li><li><p><strong>Ownership and systems thinking</strong> — you weigh upstream dependencies and downstream impact before changing anything</p></li></ul><p><strong>Highly Valued</strong></p><ul><li><p>Eval frameworks and LLM tracing (Logfire, OpenTelemetry)</p></li><li><p>Embeddings, vector search, or fuzzy matching for entity resolution at scale</p></li><li><p>Fine-tuning or distilling small models to replace expensive LLM calls</p></li><li><p>AWS at scale (S3, Lambda, Glue, ECS, RDS) and PostgreSQL</p></li><li><p>Spark or PySpark; streaming (Kafka, Kinesis, Snowpipe Streaming)</p></li><li><p>Data privacy and compliance (GDPR, SOC2, CCPA)</p></li></ul><h2>How We Build</h2><p>Firmable is an <strong>AI-native organisation</strong>. AI coding tools, automated testing, and AI-assisted review are

how we work by default. Every LLM check ships with a labelled eval set, measured precision/recall, and a prompt version you can roll back. Every LLM call in production is logged from day one; retrofitting later is not the plan.</p><p>We run lean and ship fast — small senior teams, no layers, minimal process, weekly releases moving toward daily. Teams own their stack end to end. There are no fixed hours and no handholding. If you're not already working this way, this role isn't right for you.</p><h2>Why This Role</h2><ul><li><p><strong>LLMs as production infrastructure</strong> — not a demo, not a notebook; models making millions of decisions a day on data customers pay for</p></li><li><p><strong>Greenfield harnesses</strong> — eval coverage, drift detection, and cost controls for in-pipeline LLMs are largely unbuilt; you'll ship them</p></li><li><p><strong>Scale that matters</strong> — billions of rows, 13 markets, and a dataset nobody else has</p></li><li><p><strong>Small team, massive leverage</strong> — your pipelines reach every Firmable customer, every day</p></li><li><p><strong>Competitive base + meaningful equity</strong> — a share in the upside we're building toward</p></li></ul><p><em>Firmable is an equal opportunity employer. We believe diverse teams build better products.</em></p><p>Ready to build the AI-native data platform behind the world's smartest B2B sales intelligence platform? <strong>Apply now — let's talk!</strong></p>

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