Tech Lead – Architct (Java)

About the RoleWe’re looking for a Senior Software Engineer who takes ownership seriously, someone whodesigns solutions, ships them, and stands behind them in production. You’ll work across atechnically interesting stack on systems that process millions of provider records.This is a role with real scope: you’ll influence architecture, shape engineering practices, andwork directly with product and leadership to solve hard problems in a domain that genuinelymatters.Problems You’ll SolveHealthcare’s provider data problem is a hard distributed systems problem. Hundreds of primarysources state boards, payers, federal registries each with their own schema, SLA, and failuremode. Downstream, real credentialing and network decisions depend on whatever truth we cansurface.API contract stability at velocity. You’re building a platform hundreds of integrations dependon. How do you evolve a Quarkus/REST API, adding resources, deprecating fields, shifting datamodels without breaking consumers? Contract-first design, versioning strategy, and backwardcompatibility aren’t theoretical here.Integration reliability at scale. Upstream sources go down, change schemas, and return dirtydata. 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 recordsacross hundreds of heterogeneous sources, where a wrong merge has downstreamconsequences. MDM patterns, confidence scoring, and deterministic vs. probabilistic matchingat scale.AI-augmented velocity without regression. We use Cursor and Claude Code as forcemultipliers. The engineering problem is building review culture, eval frameworks, and testcoverage that keeps quality high as output volume increases.Observability for a data platform, not just a service. Uptime isn’t enough; you need to knowwhen a provider record is stale, inconsistent, or wrong. You’ll instrument data quality andlineage, not just p99 latency.What We’re Looking ForEngineering fundamentals* 8+ years building and maintaining production-grade systems including systems whereyour API is someone else’s dependency and breaking it has real downstreamconsequences* Track record of shipping high-quality software in fast-paced environments you define thesolution, not just implement a spec* Strong engineering fundamentals: testing, clean code, maintainability, and performanceoptimization* Experience improving system reliability you’ve debugged hard production problems andmade them not happen again, with SLOs and alerting to prove it* Comfort mentoring earlier-career engineers and influencing technical directionAPI & architecture depth* Deep experience designing and evolving APIs under active consumers: versioningstrategy, 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 righttool* Hands-on experience with service-oriented and distributed architectures you’ve workedacross SOA, microservices, and event-driven patterns and can make principled tradeoffsbetween 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, notan afterthought* Experience with integration patterns at scale you’ve built or maintained systems thataggregate and normalize data from many heterogeneous upstream sources, and youunderstand the reliability and consistency tradeoffs that come with it: circuit breakers, retrystrategies, idempotency, eventual consistencyData-intensive systems• Strong data modeling instincts you understand the difference between a schema that’seasy to write and one that’s easy to query, evolve, and trust at scale* Experience with high-throughput, event-driven systems: you understand orderingguarantees, consumer lag, and failure modes in Kafka-like architectures* Strong sense of data quality: lineage, freshness, and correctness matter as much to youas throughputAI-era engineering* In an AI-augmented engineering environment, you write less and review more you’reskeptical of generated code in the right ways, and you use that leverage to ship 2–3xwhat a non-AI-fluent engineer would* Fluency with AI-assisted engineering tools (Cursor, Claude Code, MCP servers) this ispart of how we work, not a nice-to-haveCommunication & compliance* Strong written and verbal communication you can explain a technical tradeoff to anengineer and a product manager in the same conversation* Experience with sensitive data and security best practices (PII, access controls) inregulated or compliance-adjacent environmentsNice 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 messyreal-world data across disparate sources* Familiarity with healthcare credentialing workflows* Familiarity with healthcare, compliance, or regulated environmentsTechnologies & ToolsJava 21 / Quarkus · React / TypeScript · GCP (Spanner, BigQuery) · Kafka · Docker /Kubernetes · GitHub Actions · Sentry · REST / GraphQL / gRPC · Cursor / ClaudeCode / Codex