Java Spark developer

Citi; India; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Citi is seeking an experienced Java Spark Developer in India to design, develop, and maintain high-performance data processing pipelines using Core Java and Apache Spark. You will build batch and real-time streaming architectures, optimize workloads, and collaborate with Data Scientists, Cloud Engineers, and Solution Architects.

Responsibilities include designing ingestion pipelines, developing microservices with Kafka, tuning Spark jobs, and enforcing data governance and security across the

Qualifications

• 3-6 years of professional software engineering experience.
• 2-4 years building scalable distributed data processing with Java and Spark.
• Bachelor's or Master's degree in a technical field.

Responsibilities

• Design, implement, and maintain robust data ingestion and ETL/ELT pipelines using Spark (Core, SQL, Streaming) in Java or Scala interoperability.
• Develop low-latency microservices and distributed processing modules integrated with messaging platforms like Kafka.
• Build interfaces to relational databases, data lakes, and NoSQL stores; ensure data quality, governance, and security.

Skills

Core Java

Apache Spark

Distributed systems

SQL

Kafka

Maven/Gradle

Git

JUnit/Mockito

Education

Bachelor's or Master's in Computer Science / IT / Software Engineering

Tools

Jenkins

GitLab CI

GitHub Actions

Job description

We are seeking an experienced and motivated Java Spark Developer to design, develop, and maintain high-performance, large-scale data processing pipelines and distributed applications. In this role, you will leverage Core Java and Apache Spark to build resilient batch and real-time streaming data architectures, optimize distributed data workloads, and collaborate with cross-functional teams including Data Scientists, Cloud Engineers, and Solution Architects.

1. Data Pipeline & Application Development

• Design, implement, and maintain robust, scalable data ingestion and ETL/ELT pipelines using Apache Spark (Core, SQL, Streaming) written in Java (or Scala interoperability).
• Develop performant, low-latency microservices and distributed processing modules integrated with messaging platforms (e.g., Apache Kafka).
• Build and maintain interfaces to relational databases, distributed data lakes, and NoSQL stores (e.g., Hive, Cassandra, HBase, MongoDB, Delta Lake, Snowflake).

2. Performance Tuning & Optimization

• Profile, debug, and optimize Spark jobs by managing partitioning strategies, caching, broadcast variables, memory allocation (driver/executor memory), and data serialization (Kryo).
• Analyze query execution plans, DAGs, and Spark UI metrics to eliminate data skew, reduce shuffle overhead, and minimize bottleneck latencies.
• Monitor resource utilization on cluster managers such as Kubernetes, Apache YARN, or cloud-native orchestration engines.

3. Architecture & Data Modeling

• Design structured, semi-structured, and unstructured data storage schemas using columnar file formats (e.g., Parquet, ORC, Avro).
• Implement robust data validation, cleansing, data governance, and error-handling mechanisms across the ingestion lifecycle.
• Ensure data privacy and enterprise compliance by applying encryption at rest/transit and access-control policies.

4. Collaboration, CI/CD & Best Practices

• Participate in Agile/Scrum ceremonies, sprint planning, and code reviews to ensure adherence to high code quality standards.
• Write comprehensive unit, integration, and automated regression tests using frameworks such as JUnit, Mockito, and Spark Testing Base.
• Configure and maintain continuous integration and continuous deployment (CI/CD) pipelines using tools like Jenkins, GitLab CI, or GitHub Actions.

Required Qualifications & Skills

Technical Competencies

• Core Java: Deep proficiency in Java (Java 8/11/17+), including multithreading, concurrency, OOP principles, memory management, and JVM internals.
• Apache Spark: Hands-on experience developing distributed applications with Apache Spark (RDDs, DataFrames, Datasets, Spark SQL, Spark Structured Streaming).
• Distributed Ecosystem: Strong working knowledge of distributed architecture (HDFS, YARN), Hive, and distributed storage systems.
• Messaging & Streaming: Practical experience with event streaming platforms such as Apache Kafka or RabbitMQ.
• Database & Query Languages: Advanced SQL capabilities, experience with relational databases (PostgreSQL, Oracle, MySQL) and NoSQL datastores.
• Build & Version Control: Proficiency with build tools (Maven, Gradle) and Git version control workflows.
• Testing: Solid track record in Test-Driven Development (TDD) using JUnit, Mockito, and distributed testing patterns.

Professional Experience & Education

• Education: Bachelor's or Master's degree in Computer Science, Information Technology, Software Engineering, or a related technical discipline.
• Experience: 3-6 years of professional software engineering experience, with at least 2-4 years dedicated to building scalable distributed data processing applications using Java and Apache Spark.

Preferred / Desired Qualifications

• Cloud Platforms: Experience building and deploying data architectures on AWS (EMR, S3, Glue, Athena), Azure (Databricks, HDInsight, ADLS), or Google Cloud (Dataproc, BigQuery).
• Modern Lakehouse Technologies: Hands-on exposure to Apache Iceberg, Delta Lake, or Apache Hudi.
• Containerization & Orchestration: Familiarity with Docker, Kubernetes, and workflow schedulers like Apache Airflow or Luigi.
• Polyglot Exposure: Familiarity with Scala or Python (PySpark) is an added advantage.

Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.

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