Staff Data Engineer
- Model Data for Scale: Build and optimise data models across ER, document, graph, and vector structures.
- Design & Run Pipelines: Deliver ETLs and pipelines for batch, real-time, and streaming needs.
- Work Across Data Stores: Architect solutions across data warehouses (Snowflake) , data lakes , and modern platforms like Databricks .
- Streaming & Integration: Use Kafka (or similar) to manage high-volume real-time data flows.
- Catalogue & Governance: Define and deploy data catalogues , reporting standards, and best practices for analytics and performance.
- Automate by Default: Use Python and containerisation to automate workflows and deploy production-grade solutions.
- Product Thinking: Build for modularity and reusability, always balancing speed with scale and reliability.
- Strong experience in data modeling (ER, Document, Graph, Vector).
- Proven expertise with data warehouses (Snowflake) , data lakes , and Databricks or similar.
- Hands-on with ETL pipelines, batch + streaming solutions , and real-time frameworks like Kafka .
- Comfortable with diverse origin data: raw files, SQL, NoSQL, graph DBs, vector DBs.
- Strong Python skills , with containerisation (Docker/Kubernetes) experience.
- Experience deploying data catalogues and governance frameworks .
- A track record of building data solutions in a product/platform environment , at global scale .
- IoT or edge-deployed data experience.
- Additional frontend or analytics tooling knowledge.
- Exposure to mission-critical industries (life sciences, healthcare, logistics).
- Build the data backbone of a global platform, where analytics directly affect scientific progress and human health.
- Join a senior, high-calibre engineering team working closely with product and design.
- Shape data infrastructure that is modular, production-ready, and built for scale.
- Hybrid working: 3 days in our London office, balanced with flexibility.
- Competitive package, plus the opportunity to do career-defining, meaningful work .
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