Senior Java Software Engineer

Company Overview

Straive is a global leader in enterprise-grade data analytics and AI solutions, committed to empowering businesses across various industries with cutting-edge technology and expert insights. Backed by EQT, a top private equity firm, we are uniquely positioned to drive innovation through significant investments and an entrepreneurial spirit.

Our core focus is on delivering advanced Data Analytics & AI Solutions. By combining sophisticated technology with subject matter expertise, we deliver material impact on our clients' topline and streamline their operations. We specialize in providing tailored solutions across financial services, CPG, legal, pharma, life sciences, retail, energy and logistics, helping them build robust data analytics and AI capabilities.

With a client base spanning 30 countries, Straive's strategically located teams operate from eight countries and is headquartered in Singapore. This global presence enables us to offer localized expertise with a worldwide perspective.

Join Straive to be part of a dynamic team at the forefront of data analytics and AI innovation. Here, you'll have the opportunity to contribute to transformative projects, supported by significant investments and an entrepreneurial drive fueled by our partnership with EQT.

Role & Administrative Details

• Total exp: 11 years
• Location: Chennai
• Requisition IDs: R26005055, R26005056
• Title: Senior Software Engineer - Core Java & Apache Spark

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

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.

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 pipelines.