What You'll Do:

You'll be the technical lead for a backend product line in our data and analytics platform. The platform lets healthcare marketers and analysts work with privacy-protected real-world data, run queries at scale, build audiences and use AI-assisted workflows. You will own the architecture of high-throughput, secure backend services built on Java and Spring Boot, and help shape the technical roadmap.

• Provide technical leadership for your product line. Own the architecture of backend services, including service boundaries, data modelling, storage choices and scalability.
• Design multi-tenant, privacy-first data flows across services and environments, with healthcare compliance in mind.
• Lead cross-team initiatives end to end, from design through rollout, working closely with Product, Infrastructure and partner teams.
• Build and evolve data-intensive and asynchronous systems using distributed query and processing engines, workflow orchestration and messaging platforms such as Kafka. Use datastores like Redis (caching, counters, rate limiting, locking, pub/sub) where they fit, alongside relational and analytical stores.
• Lead the integration of AI into our products and engineering workflows. This includes LLM-powered features, agentic workflows, retrieval over metadata or documents, and tool integrations via the Model Context Protocol (MCP). Design for grounded output, human approval, cost control and measurable quality.
• Establish engineering best practices for API design, code quality, testing, security and data privacy.
• Design observability (metrics, logging, tracing, alerting) with tools such as Grafana and Prometheus. Lead deep-dive investigations into production issues and run blameless postmortems.
• Mentor senior and junior engineers through design reviews, code reviews and pair programming.
• Champion responsible AI-assisted development and set standards for how engineers use coding agents and AI tools.
• Drive knowledge sharing through documentation, design forums and internal talks.

Who You Are:

• 8+ years of backend engineering experience, with a track record of designing and delivering large-scale distributed systems in production.
• Deep expertise in Java (17+) and the Spring ecosystem (Spring Boot, Spring Data), including JVM internals, concurrency and performance tuning.
• Solid experience with relational and NoSQL or analytical databases (such as MySQL, PostgreSQL, Cassandra or ClickHouse), including schema design and query optimisation.
• Working knowledge of Redis and how to apply it beyond caching, with an understanding of its trade-offs at scale.
• Experience with big-data or analytical query engines (such as Spark) and data lake storage, or a strong willingness to go deep on them.
• Strong grounding in microservices, API design (REST and GraphQL) and distributed systems fundamentals: consistency, idempotency, partitioning and fault tolerance.
• Strong security fundamentals: authentication and authorization, tenant isolation, injection prevention and safe handling of sensitive data.
• Experience with messaging, streaming and workflow-orchestrated systems (such as Kafka or Airflow), and with designing resilient, idempotent, event-driven architectures.
• Experience with cloud platforms (GCP or AWS), containerisation (Docker, Kubernetes) and owning CI/CD pipelines.
• Working knowledge of practical AI engineering: LLM APIs, prompt and context engineering, agentic systems, RAG, MCP, and evaluating AI quality and reliability.
• Hands-on daily use of AI coding tools (such as Claude Code, GitHub Copilot or Cursor), with good judgment about where they help and where human review matters.
• Proven ability to lead technical work across teams, influence without direct authority and mentor senior engineers.
• A clear communicator who can explain trade-offs to technical and non-technical stakeholders.
• Experience with healthcare data platforms, data clean rooms or ad tech (programmatic, DSP/SSP) is a plus.
• Experience building production LLM features, including guardrails, cost and latency optimisation, and observability for AI workloads is a plus.
• Familiarity with Spark or Flink at scale is a plus.
• Open-source contributions, technical writing or speaking is a plus.