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Senior Software Engineer - Core Java & Apache Spark

India, Chennai, Pune · Job Posted May 17, 2026
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Job Description

We are hiring an elite Senior Software Engineer to build and scale our core data processing and application infrastructure. This role demands deep, hands-on expertise in the Java ecosystem and distributed computing with Apache Spark. You will be responsible for the architecture, design, and implementation of mission-critical systems that process massive datasets, requiring a mastery of concurrency, JVM internals, and modern cloud-native patterns.

Job Responsibility

  • Architect & Build: Design and construct high-throughput, low-latency data processing pipelines using Apache Spark and the Spring ecosystem
  • Performance Engineering: Dive deep into JVM internals, garbage collection tuning, and Spark job optimization to maximize performance and resource efficiency
  • Distributed Systems Design: Implement scalable, resilient, and transactional architectures leveraging container orchestration (Kubernetes/OpenShift) and distributed data stores
  • Code & Design Excellence: Champion and enforce best practices in software engineering, including SOLID principles, advanced design patterns, Domain-Driven Design (DDD), and Test-Driven Development (TDD)
  • Database Mastery: Engineer and optimize data models for both relational and NoSQL databases, ensuring data integrity, performance, and scalability
  • CI/CD Automation: Own and enhance CI/CD pipelines for automated build, test, and deployment of Java applications and Spark jobs in a containerized environment
  • Technical Leadership: Lead design and code reviews, mentor junior engineers, and drive the adoption of new technologies and architectural patterns across the team

Requirements

  • Core Java & JVM: Expert-level proficiency in Java, including the Collections Framework, Lambdas, and the Java Concurrency API. Demonstrable experience tuning the JVM and troubleshooting memory/GC issues
  • Apache Spark: Proven, hands-on experience developing, deploying, and tuning complex Spark applications for large-scale data transformation and analysis
  • Spring Ecosystem: Extensive, practical experience with the Spring Framework, particularly Spring Boot, Spring Data, and Spring Batch in a production environment
  • Data Structures & Algorithms: Deep understanding of fundamental data structures and algorithms, with a focus on their application in distributed computing and performance-critical systems
  • Containerization & Cloud-Native: Hands-on experience with Docker for building images and Kubernetes/OpenShift for deploying and managing distributed applications
  • Database Engineering: Strong command of SQL and relational database design, including transaction management and indexing. Experience with at least one production NoSQL database (MongoDB, Graph DB, etc.)
  • Architectural Design: Practical application of OOP, SOLID, and DDD principles to build maintainable and scalable systems. You write tests first (TDD) and believe in robust, automated testing
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