Senior Software Engineer - Core Java & Apache Spark

citi

Chennai, India 8 Years Exp Posted 1h ago

Languages & Runtimes: Java (8+), SQL, JVM
Frameworks & Libraries: Spring (Boot, Data, Security, Batch, Integration), Apache Spark (RDD, Spark SQL, DataFrames/DataSets)
Big Data Ecosystem: Hadoop, Hive, Impala, Spark Tuning & Optimization
Databases: Relational (e.g., 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

Key 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.

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.