Tech Lead - 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.

Responsibilities:

• Healthcare's provider data problem is a hard distributed systems problem. Hundreds of primary sources, state boards, payers, and federal registries, each with their own schema, SLA, and failure mode. Downstream, real credentialing and network decisions depend on whatever truth we can surface.
• API contract stability at velocity. You're building a platform hundreds of integrations depend on. How do you evolve a Quarkus/REST API, adding resources, deprecating fields, and shifting data models without breaking consumers? Contract-first design, versioning strategy, and backward compatibility aren't theoretical here. Integration reliability at scale. Upstream sources go down, change schemas, and return dirty data. You'll build the patterns that absorb that chaos: idempotent consumers, dead-letter queues, circuit breakers, and reconciliation pipelines on top of Kafka and Spanner.
• Entity resolution on messy real-world data. Deduplicating and reconciling provider records across hundreds of heterogeneous sources, where a wrong merge has downstream consequences. MDM patterns, confidence scoring, and deterministic vs. probabilistic matching at scale.
• AI-augmented velocity without regression. We use Cursor and Claude Code as force multipliers. The engineering problem is building review culture, eval frameworks, and test coverage that keeps quality high as output volume increases.
• Observability for a data platform, not just a service. Uptime isn't enough; you need to know when a provider record is stale, inconsistent, or wrong. You'll instrument data quality and lineage, not just p99 latency.

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 optimization.
• 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 and 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 normalize data from many heterogeneous upstream sources, and you understand the reliability and consistency tradeoffs that come with it: circuit breakers, retry strategies, idempotency, and eventual consistency.
• Data-intensive systems. Strong data modeling 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 skeptical 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, and 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 behavior, 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, and Codex.