Sourcebae
Website:
sourcebae.com
Company:
https://www.linkedin.com/company/sourcebae
Seniority: Mid-Senior level
Industries: IT Services and IT Consulting
Job details:
Tech Lead – Architect (Java)
Remote · Full-Time
Candidates have to go one week onsite at location Jaipur then fully remote
Technologies & Tools
Java 21 / Quarkus · React / TypeScript · GCP (Spanner, BigQuery) · Kafka · Docker /
Kubernetes · GitHub Actions · Sentry · REST / GraphQL / gRPC · Cursor / Claude
Code / Codex
About the Role
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.
Problems You’ll Solve
Healthcare’s provider data problem is a hard distributed systems problem. Hundreds of primary
sources state boards, payers, 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, 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.
What We’re Looking For
Engineering fundamentals
• 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 & 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 normalize 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 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, MCP servers) this is
part of how we work, not a nice-to-have
Communication & 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
Click on Apply to know more.