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Bengaluru, Karnataka, India
Hybrid
About the Role
This is a Lead Developer role with a strong focus on performance engineering. You will spend your time building and optimizing high-throughput, low-latency systems at scale — writing production Java code, hunting down bottlenecks, and making the platform faster and more resilient.
Performance is the through-line of everything you do: you'll set performance requirements, design and execute the testing that validates them, profile and tune the system end-to-end, and build the engineering practices that keep it fast release over release. You'll work on services built on Kafka, MongoDB, Elasticsearch, Debezium, and Spring Boot running on AWS.
The “lead” in this role is technical, not managerial. You lead through hands-on work, strong engineering judgment, and by raising the performance bar for the people around you — through design and code reviews, mentoring, and setting standards — while remaining a core contributor in the codebase.
Remain hands-on: write, review, and debug production-quality Java code as a core, day-to-day part of the role.
Lead the design and development of scalable, high-performance Java services and APIs.
Own the end-to-end performance engineering strategy — from setting performance requirements and SLAs/SLOs to validating them under load.
Design, build, and execute performance test suites (load, stress, soak, spike, and scalability tests) and integrate them into CI/CD pipelines.
Profile applications to identify bottlenecks across CPU, memory, GC, threading, I/O, event streaming (Kafka), data stores (MongoDB, Elasticsearch), CDC (Debezium), and network layers, and drive them to resolution.
Tune the JVM (garbage collection, heap sizing, thread pools) and application configuration for optimal throughput and latency.
Analyze test results, produce clear performance reports, and translate findings into concrete engineering actions.
Establish performance benchmarks and guardrails, and prevent regressions through automated performance gates.
Mentor and provide technical leadership to a team of developers; conduct design and code reviews with a performance-first lens.
Collaborate with SRE/DevOps on capacity planning, observability, and production performance monitoring.
Contribute to architectural decisions that affect scalability, reliability, and cost efficiency.
8+ years of software development experience with strong, current, hands-on expertise in Java (Java 11/17+) and the JVM — you are still actively coding and comfortable in the codebase every day.
Proven experience designing and operating modern cloud distributed systems at scale on AWS.
Hands-on expertise across the following stack: Apache Kafka — event streaming, partitioning, consumer groups, and tuning for high-volume throughput.
MongoDB — data modeling, indexing, and query/performance tuning under load.
Elasticsearch — indexing strategy, query performance, and cluster tuning.
Debezium — change data capture (CDC) pipelines and connector configuration.
Spring Boot — building resilient, production-grade microservices and REST APIs.
Proven experience in performance engineering — designing and executing performance testing programs, not just running scripts.
Hands-on experience with performance testing tools such as JMeter, Gatling, k6, or LoadRunner.
Strong skills in profiling and diagnostics using tools such as JProfiler, YourKit, async-profiler, VisualVM, or Java Flight Recorder.
Deep understanding of JVM internals, garbage collection tuning, memory management, and concurrency.
Solid experience diagnosing performance issues across the full stack — application, event streaming, database, search, caching, and messaging.
Experience running services in containerized environments (Docker, Kubernetes).
Familiarity with observability and APM tooling (e.g., Grafana, Prometheus, Datadog, New Relic, Dynatrace) for monitoring and root-cause analysis.
Demonstrated technical leadership: mentoring engineers, leading design discussions, and driving quality standards.
Experience with high-volume data pipelines and CDC-driven architectures at scale.
Experience with capacity planning and cost optimization on AWS.
Familiarity with CI/CD pipelines and integrating performance gates into automated delivery.
Deep experience tuning distributed data stores under heavy, sustained load.
Contributions to open-source, technical writing, or conference speaking on performance or distributed-systems topics.