Lead Software Engineer - Performance

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!

We are seeking a talentedLead Software Engineer – Performance to deliver roadmap features of theUnified Asset Inventory ETM Platform, which helps customersMeasure, Communicate, and Eliminate Cyber Risks.

You will lead performance engineering efforts acrossJava microservices, Spark, Kafka, Elasticsearch, and middleware APIs, ensuring that our real-time data pipelines and services meet enterprise-grade SLAs.

As part of our high-performing engineering team, you will design and execute performance testing strategies, identify system bottlenecks, and work with development teams to implement performance improvements that support the processing ofbillions of cybersecurity events per day across our data platform.

Responsibilities

• Own the performance strategy across distributed systems, includingSpring Boot microservices, Hadoop, Spark, Kafka, Elasticsearch/OpenSearch, Big Data components, and APIs for each release.
• Demonstrate strong expertise in performance engineering, including analysis ofHeap Dumps, Thread Dumps, GC Logs, and CPU Profiling Reports, with hands-on experience usingApache JMeter, AppDynamics, Grafana, and Prometheus, and a deep understanding ofJVM architecture, Garbage Collection, Threading, and Concurrency.
• Define, develop, and executeperformance test plans, load tests, stress tests, and soak tests.
• Create realistic performance test scenarios for data pipelines and microservices based onproduction-like workloads.
• Proactively identify bottlenecks, resource contention, and latency issues using tools such asJMeter, Spark UI, Kafka Manager, Elastic Monitoring, and AppDynamics.
• Provide deep-dive analysis and recommendations for tuning and scalingSpark jobs, Kafka topics/partitions, Elasticsearch queries, and API endpoints.
• Collaborate with developers, architects, and infrastructure teams to integrate performance feedback into system design and implementation.
• Simulate and benchmark real-time and batch data flows at scale using synthetic and production-like datasets, and own the performance framework end-to-end for thesynthetic data generator.
• Lead the initiative to build aperformance testing framework that integrates with CI/CD pipelines.
• Establish and track SLAs forthroughput, latency, CPU/memory utilization, and Garbage Collection.
• Create performance dashboards and visualizations usingPrometheus/Grafana, Kibana, or equivalent tools.
• Document performance test findings and prepare technical reports for leadership and engineering teams.
• Recommend performance optimizations toDevelopment and Platform teams.
• Take responsibility for optimizing theoverall cost.
• Contribute to feature development and fixes in addition to performance benchmarking.

Required Qualifications

• Bachelor's degree inComputer Science, Engineering, or a related field.
• 8+ years of overall experience in distributed systems and backend performance engineering.
• 4+ years of Java development experience withMicroservices architecture.
• Proficiency in scripting usingPython and Bash for automation and test data generation.
• 4+ years of hands-on experience withApache Spark, including performance tuning, memory management, and DAG optimization.
• 3+ years of experience withKafka, including topic optimization, producer/consumer tuning, and lag monitoring.
• 3+ years of experience withElasticsearch/OpenSearch, including query profiling, indexing strategies, and cluster optimization.
• 3+ years of experience with performance testing tools such asJMeter or similar.
• Excellent programming and design skills, with hands-on experience withSpring and Hibernate.
• Deep understanding ofmiddleware and microservices performance, including REST APIs.
• Strong knowledge ofprofiling, debugging, and observability tools, such as Spark UI, Athena, Grafana, and ELK.
• Experience designing and runningbenchmarks at scale for high-throughput environments in PBs.
• Experience withcontainerized workloads and performance testing inKubernetes/Docker environments.
• Solid understanding ofcloud-native architecture (OCI) and distributed systems design.
• Strong knowledge ofLinux operating systems and performance-related improvements.
• Familiarity withCI/CD integration for performance testing, such as Jenkins and GitHub.
• Knowledge ofdata lake architecture, caching solutions, and message queues.
• Strong communication skills and experience influencingcross-functional engineering teams.

Preferred Qualifications

• Prior experience withanalytics platforms on Big Data would be a strong plus.