Java & JVM backend engineering
Scale JVM services without the hiring backlog. Java engineers with production scars.
Consumer lag, exhausted connection pools, and a risky Flyway script can stall a release train faster than any feature debate. We staff Java engineers who trace Kafka issues to poison messages, size HikariCP against RDS limits, and ship migrations with rollback in mind. Services stay observable with Micrometer and OpenTelemetry, and architecture choices favor what you can operate on-call, not what reads well in a slide deck.
Discuss your Java roadmap@RestController@RequestMapping("/api/v1/invoices")public class InvoiceController { private final InvoiceService invoiceService; private final EventPublisher events; @PostMapping public ResponseEntity<InvoiceResponse> create(@Valid @RequestBody InvoiceRequest req) { Invoice invoice = invoiceService.createDraft(req); events.publish(new InvoiceCreated(invoice.getId(), req.customerId())); return ResponseEntity.accepted().body(InvoiceResponse.from(invoice)); } @GetMapping("/{id}") public InvoiceResponse get(@PathVariable UUID id) { return InvoiceResponse.from(invoiceService.require(id)); }}Core stack
- Spring Boot
- Kafka
- PostgreSQL / MySQL
- Maven / Gradle
- JUnit & Testcontainers
- Micrometer & tracing
5+
Average years in production Java
Senior engineers who've owned services on-call, not tutorial-only Spring experience.
Deep-Dive Tech Stack
JVM backends hinge on how Spring services, Kafka flows, persistence, and containers interact. We match on the patterns you run so tuning, migrations, and event-driven design stay coherent from local dev through production on-call.
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Spring Boot
REST APIs, OAuth2 security, actuator health checks, and profile-driven config for environment parity. Under load they catch N+1 fetch storms, mis-sized thread pools, and actuator endpoints exposed without auth before customers do.
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Kafka
Producer acks, consumer group behavior, idempotent producers, and dead-letter topics with replay tooling. Offset lag, partition skew, and exactly-once semantics for billing events are debugged with intent, not broker configs copied from a tutorial.
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PostgreSQL / MySQL
JPA and Hibernate with explicit fetch plans and Flyway or Liquibase migrations reviewed for lock duration. Connection pools match RDS limits; indexes follow EXPLAIN evidence instead of Spring defaults that fail under traffic spikes.
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Maven / Gradle
Multi-module builds, BOM dependency pinning, and reproducible CI artifacts with SBOM output. Slow integration modules are isolated from fast unit-test loops so developers get signal without waiting on the full graph every commit.
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JUnit & Testcontainers
Integration tests against real Kafka and Postgres in CI. Mocks that pass while production fails are replaced with ephemeral dependencies that catch schema drift before merge.
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Micrometer & tracing
Metrics export to Prometheus, timers on critical paths, and trace propagation across sync and async boundaries. Dashboards tie to SLOs on user journeys, not vanity heap graphs.
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Docker & JVM tuning
Container-aware heap settings, layered JAR images, and GC choice matched to latency budgets. OOMKilled pods from heap limits that ignore cgroup boundaries get fixed at the root, not with bigger boxes alone.
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Redis & caching
Session stores, distributed locks, and cache-aside patterns with TTL and invalidation on write. Stampede and hot-key issues get tuned from metrics before Black Friday traffic, not after checkout latency spikes in production.
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Hibernate & JPA tuning
Fetch plans, batch inserts, and second-level cache where appropriate, with N+1 queries caught in review and integration tests. Lazy-loading surprises in async endpoints get fixed at the mapping layer, not with blanket eager fetch everywhere.
Numbers JVM leads track
- Average years in production Java
- 5+
- Typical ramp to first merged PR
- 2–3 wks
- Engagement retention past 6 months
- 90%+
- From access to production deploy
- Weeks
Senior engineers who've owned services on-call, not tutorial-only Spring experience.
Includes repo setup, CI green builds, and a scoped starter ticket in your sprint board.
Matching on stack depth and communication style upfront keeps churn low.
For scoped microservices with existing platform patterns and CI in place.
Java staffing questions we answer upfront
How do you handle time-zone crossovers?
We align 3–4 hours of overlap with US or EU core teams for standups, PR reviews, and incident calls. Async updates go to Slack or your tracker with written context so decisions don't stall overnight.
Do your engineers work in monoliths or microservices?
Both. We staff against what you run today: modular monoliths, service meshes, or a hybrid mid-migration. We don't force a rewrite to justify the engagement.
What is your code review process?
Every PR gets peer review from a senior engineer on our side before it hits your repo. We check test coverage, API contract breaks, and migration safety. Your tech lead stays the final approver.
Can engineers join our existing sprint cadence?
Yes. They use your Jira, Linear, or GitHub Projects from day one. We don't add a parallel process layer.
Who owns the IP and commits?
You do. All work lands in your repositories under your CLA or employment terms. We don't retain code or reuse your business logic across clients.
