Java Spark developer

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.

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