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 pair programming using AI coding tools (such as Claude Code, GitHub Copilot or Cursor), with complete ownership of reviewing and debugging it. Should have good judgment about where they help and where human review matters.
• Has shipped features using AI pair programming, and not just experimented.
• Understands security and data-privacy guardrails while using AI.
• 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.