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Lead Software Engineer - Performance

Qualys -
Pune, Maharashtra

Apply Now

Job details

Full-time

Qualifications

BCS
CI/CD
Performance tuning
Elasticsearch
Cloud architecture
Computer Science
Kubernetes
Big data
Spark
Git
Performance testing
Java
Bash (Unix shell)
Microservices
Docker
Bachelor's degree
Distributed systems
Continuous integration
REST
Scripting
GitHub
APIs
Linux
Kafka
Middleware
Jenkins
Communication skills
Python
Spring framework
Debugging
Hibernate
Hadoop

Full job description

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

Description:

We are seeking a talented Lead Software Engineer – Performance to deliver roadmap features of Enterprise TruRisk Platform which would help customers to Measure, Communicate and Eliminate Cyber Risks.

You will lead the performance engineering efforts across 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 billions of cyber security events processing a day across our data platform.

Responsibilities:

Own the performance strategy across distributed systems which includes Hadoop, Spark, Kafka, Elasticsearch/OpenSearch, Big Data Components and APIs for each release.

Define, develop, and execute performance test plans, load tests, stress tests, and soak tests.

Create realistic performance test scenarios for data pipelines and microservices based on production-like workloads.

Proactively identify bottlenecks, resource contention, and latency issues using tools such as JMeter, Spark UI, Kafka Manager, Elastic Monitoring and App Dynamics.

Provide deep-dive analysis and recommendations on tuning and scaling Spark jobs, Kafka topics/partitions, ES queries, and API endpoints.

Collaborate with developers, architects, and infrastructure teams to integrate performance feedback into design and implementation.

Simulate and benchmark real-time and batch data flow at scale using synthetic and production-like datasets and own this framework end to end for synthetic data generator.

Lead the initiative to build a performance testing framework that integrates with CI/CD pipelines.

Establish and track SLAs for throughput, latency, CPU/memory utilization and Garbage collection.

Create performance dashboards and visualization using Prometheus/Grafana, Kibana, or equivalent.

Document performance test findings and create technical reports for leadership and engineering teams.

Recommend performance optimization to Dev and Platform groups.

Responsible for optimizing the overall cost.

Contribute to feature development and fixes apart from performance benchmarking.

Qualifications:

Bachelor's degree in computer science, Engineering, or related field.

8+ years of overall experience in distributed systems and backend performance engineering.

4+ years of JAVA development experience with Microservices architecture.

Proficient in scripting (Python, Bash) for automation and test data generation.

4+ years of hands-on experience with Apache Spark – performance tuning, memory management, and DAG optimization.

3+ years of experience with Kafka – topic optimization, producer/consumer tuning, and lag monitoring.

3+ years of experience with Elasticsearch/OpenSearch – query profiling, indexing strategies, and cluster optimization.

3+ years of experience with performance testing tools such as JMeter or similar.

Excellent programming and designing skills and Hands-on experience on Spring, Hibernate.

Deep understanding of middleware and microservices performance including REST APIs.

Strong knowledge of profiling, debugging, and observability tools (e.g., Spark UI, Athena, Grafana, ELK).

Experience designing and running benchmarks at scale for high-throughput environments in PBs.

Experience with containerized workloads and performance testing in Kubernetes/Docker environments.

Solid understanding of cloud-native architecture (OCI) and distributed systems design.

Strong knowledge of Linux operating systems and performance related improvements.

Familiarity with CI/CD integration for performance testing (e.g., Jenkins, GitHub).

Knowledge of data lake architecture, caching solutions, and message queues.

Strong communication skills and experience influencing cross-functional engineering teams.

Additional Plus Competencies:

Prior experience in any analytics platform on Big Data would be a huge plus.

Apply Now

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