Data Engineer - Data Platform

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