Java Spark Developer

Capgemini; Pune District, Chennai; Hybrid; INR 900,000 - 1,900,000; Full time

Job summary

Capgemini in Pune, India invites a Java + Spark Developer to join our data engineering team. The role requires 5-8 years of experience and involves building scalable data processing applications with Java and Apache Spark, plus REST APIs with Spring Boot.

You will work with Hadoop ecosystem, Kafka, and cloud services, optimize Spark jobs, and ensure robust CI/CD practices. The ideal candidate will collaborate with data engineers, architects, and business teams to deliver reliable data solutions

Qualifications

• Strong experience in Core Java (Java 8/11/17).
• Hands‑on experience with Apache Spark (Spark Core, Spark SQL, DataFrames, Datasets).
• Expertise in Spring Boot and microservices architecture.
• Strong SQL and database knowledge (Oracle, PostgreSQL, Hive, etc.).
• Experience with Kafka, Hadoop ecosystem, and distributed computing concepts.
• Good understanding of multithreading, collections, JVM tuning, and performance optimization.

Responsibilities

• Design, develop, and maintain scalable data processing applications using Java and Apache Spark.
• Build batch and real-time data pipelines for large-scale data processing.
• Develop RESTful APIs and microservices using Spring Boot.
• Optimize Spark jobs for performance, scalability, and reliability.
• Work with Hadoop, Hive, HDFS, Kafka and cloud services.
• Collaborate with data engineers, architects, and business teams to deliver data solutions.
• Troubleshoot production issues and perform root cause analysis.
• Follow coding standards, unit testing, CI/CD, and DevOps best practices.

Skills

Core Java

Apache Spark

Spring Boot

SQL/Databases

Kafka/Hadoop

Multithreading/JVM tuning

Job description

Role: Java + Spark Developer

Experience: 5-8 Years

Key Responsibilities

• Design, develop, and maintain scalable data processing applications using Java and Apache Spark.
• Build batch and real-time data pipelines for large-scale data processing.
• Develop RESTful APIs and microservices using Spring Boot.
• Optimize Spark jobs for performance, scalability, and reliability.
• Work with distributed data platforms such as Hadoop, Hive, HDFS, Kafka, and cloud services.
• Collaborate with data engineers, architects, and business teams to deliver data solutions.
• Troubleshoot production issues and perform root cause analysis.
• Follow coding standards, unit testing, CI/CD, and DevOps best practices.

Mandatory Skills

• Strong experience in Core Java (Java 8/11/17).
• Hands‑on experience with Apache Spark (Spark Core, Spark SQL, DataFrames, Datasets).
• Expertise in Spring Boot and Microservices architecture.
• Strong SQL and database knowledge (Oracle, PostgreSQL, Hive, etc.).
• Experience with Kafka, Hadoop ecosystem, and distributed computing concepts.
• Good understanding of multithreading, collections, JVM tuning, and performance optimization.