Java Spark Professional

Job highlights

• Containerization Cloud-Native: Hands-on experience with Docker for building images and Kubernetes / OpenShift for deploying and managing distributed applications
• Demonstrable experience tuning the JVM and troubleshooting memory / GC issues
• Experience with at least one production NoSQL database (MongoDB,Graph DB,etc.)
• Required Technical Qualifications

Job Summary

Senior Software Engineer - Core Java Apache Spark (C13- VP), C12-AVP

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.

Core Tech Stack

• Languages Runtimes: Java (8+), SQL, JVM

• Big Data Ecosystem: Hadoop, Hive, Impala, Spark Tuning Optimization

• Databases: Relational (PostgreSQL, Oracle), NoSQL (MongoDB, Graph DB)

• Messaging Middleware: JMS, Solace

• Containerization Orchestration: Docker, Kubernetes, OpenShift

• Build CI/CD: Maven, Gradle, Jenkins, Git

• Code Quality Security: SonarQube, TDD (JUnit/Mockito), Secure Coding Practices

• Frameworks Libraries: Spring (Boot, Data, Security, Batch, Integration), Apache Spark (RDD, Spark SQL, DataFrames/DataSets)

  Responsibilities

• 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.

  Required Technical Qualifications

• 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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