Senior Data Engineer

Building and owning data pipelines and the platform layer that AI products across the company depend on Analytics on LLM usage — cost, token and cache economics, financial reporting on AI spend RAG systems: contextual knowledge bases, vector and semantic search infrastructure Making the platform operable — profiling, monitoring, observability of the pipelines you own Building interfaces and services on top of the data, not just landing it (auth, APIs, interactive access for consumers) Strong commercial data engineering experience with a modern stack — e.g. Databricks, dbt, Airflow, Dagster, Greenplum, Snowflake, or similar Confident Python, plus a working backend mindset: how authorisation works, how a service is exposed and consumed. We're looking for a data engineer who is comfortable building something interactive, not only delivering datasets downstream Practical understanding of how to profile and monitor data systems in production Real understanding of the AI/LLM layer — vector databases, embeddings, contextual and semantic search, RAG architectures, how autonomous agents work. This is an AI Platform team: we expect conceptual grasp of these technologies, not just having used a coding assistant Cloud experience (AWS ideally)