Tech Lead Architct (Java)

We're looking for a Senior Software Engineer who takes ownership seriously, someone who designs solutions, ships them, and stands behind them in production. You'll work across a technically interesting stack on systems that process millions of provider records. This is a role with real scope: you'll influence architecture, shape engineering practices, and work directly with product and leadership to solve hard problems in a domain that genuinely matters.

The candidate will have responsibilities across the following functions:

API Contract Stability at Scale:

• Design and evolve Quarkus/REST APIs without breaking existing consumers.
• Drive contract-first design, versioning strategies, backward compatibility, and API evolution.

Reliable Data Integration:

• Build resilient integration patterns for unreliable upstream systems.
• Implement idempotent consumers, dead-letter queues, circuit breakers, reconciliation pipelines, Kafka-based processing, and Spanner-backed storage.

Entity Resolution:

• Deduplicate and reconcile provider records across hundreds of heterogeneous data sources.
• Design MDM patterns, confidence scoring, and deterministic/probabilistic matching systems.

AI-Augmented Engineering:

• Leverage Cursor and Claude Code to improve engineering velocity while maintaining quality.
• Build strong review practices, evaluation frameworks, and comprehensive test coverage.

Observability for Data Platforms:

• Go beyond service uptime by monitoring data freshness, lineage, quality, and consistency.

Requirements:

• 8+ years building and maintaining production-grade systems, including systems where your API is someone else's dependency and breaking it has real downstream consequences.
• Track record of shipping high-quality software in fast-paced environments; you define the solution, not just implement a spec.
• Strong engineering fundamentals: testing, clean code, maintainability, and performance optimisation.
• Experience improving system reliability: you've debugged hard production problems and made them not happen again, with SLOs and alerting to prove it.
• Comfort mentoring earlier-career engineers and influencing technical direction.

API and architecture depth:

• Deep experience designing and evolving APIs under active consumers: versioning strategy, backward compatibility, and contract-first thinking
• Fluency across API paradigms: REST, GraphQL, gRPC, and async/event-driven APIs (webhooks, Kafka topics as contracts) and the judgment to know when each is the right tool.
• Hands-on experience with service-oriented and distributed architectures; you've worked across SOA, microservices, and event-driven patterns and can make principled tradeoffs between them based on coupling, latency, and operational complexity.
• Experience designing for API consumers as first-class stakeholders: SDK ergonomics, pagination, rate limiting, error semantics, and documentation as part of the contract, not an afterthought.
• Experience with integration patterns at scale: you've built or maintained systems that aggregate and normalise data from many heterogeneous upstream sources, and you understand the reliability and consistency tradeoffs that come with it: circuit breakers, retry strategies, idempotency, eventual consistency.

Data-intensive systems:

• Strong data modelling instincts; you understand the difference between a schema that's easy to write and one that's easy to query, evolve, and trust at scale. Experience with high-throughput, event-driven systems: you understand ordering guarantees, consumer lag, and failure modes in Kafka-like architectures.
• Strong sense of data quality: lineage, freshness, and correctness matter as much to you as throughput.

AI-era engineering:

• In an AI-augmented engineering environment, you write less and review more; you're sceptical of generated code in the right ways, and you use that leverage to ship 2-3x what a non-AI-fluent engineer would.
• Fluency with AI-assisted engineering tools (Cursor, Claude Code, MCP servers); this is part of how we work, not a nice-to-have.

Communication and compliance:

• Strong written and verbal communication; you can explain a technical tradeoff to an engineer and a product manager in the same conversation.
• Experience with sensitive data and security best practices (PII, access controls) in regulated or compliance-adjacent environments.

Nice to Have:

• Experience building or operating AI/LLM pipelines in production (not just prototypes), including eval frameworks, fallback behaviour, and monitoring for non-deterministic outputs.
• Experience with entity resolution or MDM systems at scale, deduplicating messy real-world data across disparate sources.
• Familiarity with healthcare credentialing workflows.
• Familiarity with healthcare, compliance, or regulated environments.
• Technologies and Tools: Java 21 / Quarkus React / TypeScript GCP (Spanner, BigQuery) Kafka Docker / Kubernetes GitHub Actions Sentry REST / GraphQL / gRPC Cursor / Claude Code / Codex.