Senior/Lead Data Engineer

Lead Data Engineer | Python, Kafka, Vector Databases | First Senior Data Hire at a Pre-Series-A AI Platform

• Salary: from c. £85k, plus early-stage equity
• Location: Victoria, central London. 4 days a week in the office, Wednesdays from home
• Right to work: existing UK right to work, no sponsorship
• Also hiring: a Senior Data Engineer on the same team, one rung down

I’m working with an AI startup in London that’s automating the admin out of property management. Unglamorous problem in a very large market, which is usually where the money turns out to be, and they’re already live with pilot customers managing thousands of units.

You’ll be their first senior data hire. The architecture is close to a blank slate, so you’re the one deciding what it looks like: Python, Postgres, Kafka and vector databases doing the retrieval behind a product people use every day.

Fair warning on the vector database side, that’s the bit they’re strictest on and they’ve turned good engineers down for being light there. Lead with it.

Four days a week in the office in Victoria with Wednesdays from home. It’s a dozen or so engineers, and they want everyone in the room while it’s this early.

What They’re Looking For (Non-Negotiables)

• Vector databases: Qdrant, Milvus, pgvector or similar, used in a live system. No flex here
• Python: strong and production-grade
• 7+ years in data or backend engineering
• Data pipelines: built and owned at scale
• Postgres (MySQL considered) and NoSQL
• Kafka or similar
• Real-time streaming
• UK right to work: no sponsorship on this one, and no flex on it either
• Permanent hire, not a contract engagement

What You’ll Work With

• MongoDB
• Spark
• Airflow
• Elasticsearch / OpenSearch
• Pandas and Polars
• Machine learning (ML) data workflows, alongside the AI and backend engineers
• You’ll own the architecture end to end, tooling and standards included

Nice to Haves

• AI / ML platform experience
• Event-driven architectures
• Cloud: GCP, AWS or Azure
• Early-stage or high-growth startup experience

Why Join / Projects

You’ll build the pipelines and infrastructure the whole product runs on: batch and real-time ingestion, transformation, storage, the lot, starting from close to nothing. Vector search, retrieval and the ML pipelines are yours to design, then make fast as usage climbs.

After that you set the reliability, security and governance standards, hire the people who work to them, and run the function you built. Flat structure, leads are senior ICs, and you’ll be in the room with the founders and the Head of Engineering when the decisions get made. There’s a head of data seat at the end of this if you want it.

They’re pre-Series-A with live pilots and a revenue push behind them, so you’re in before the raise rather than after it, which is the point of taking equity in the first place.

Employee Benefits

• Equity: a meaningful stake, in line with joining as an early senior hire
• One day a week from home, Wednesdays

Then the engineers, the Head of Engineering, and the founders.

The Senior Data Engineer role is worth a word too. Same team, same stack, same office, one rung down, so you’re building the pipelines alongside the lead instead of defining the architecture yourself. If you’re five years or so in and this ad reads like a stretch, that’s the one to ask me about. Same call either way.

Lead Data Engineer | Python, Kafka, Vector Databases | First Senior Data Hire at a Pre-Series-A AI Platform