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Java Developer at SAIC

, Virginia, United States -

Full Time

Start Date

Immediate

Expiry Date

18 May, 26

Salary

0.0

Posted On

17 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Java, PySpark, Spark, Databricks, Java 8+, OOP, Functional Programming, Data Pipelines, IRMF, BMF, IMF, Performance Optimization, Git, Maven/Gradle, DevOps, Debugging

Industry

Defense and Space Manufacturing

Description

SAIC is looking for a Java Developer who will be responsible for converting existing PySpark codebases into optimized Java-based Spark applications. This role includes developing, refactoring, and maintaining scalable data processing solutions developed on the Databricks platform (or similar Spark execution environments). Key Responsibilities: · Convert existing PySpark applications into equivalent, efficient Java Spark implementations · Design, develop, and maintain scalable Spark-based data pipelines · Implement data processing logic using Java 8+ with best practices in OOP and functional programming · Integrate solutions with IRS datasets including IRMF, BMF, and IMF · Optimize Spark jobs for performance, maintainability, and cost-efficiency · Collaborate across development, data engineering, and architecture teams · Troubleshoot and debug Spark workloads in distributed environments · Ensure compliance with IRS data handling, security, and governance policies Required QualificationsRequired: Bachelor’s degree in Computer Science, Information Systems, or a related field. Active MBI Clearance 5+ years of professional experience in a data engineering or software development role. Advanced expertise in: · IRS datasets (IRMF, BMF, IMF) and tax system data structures. · Java 8+ (experience with functional programming, Streams API, Lambdas). · Apache Spark (Spark Core, Spark SQL, DataFrame APIs, performance tuning). · Big data ecosystems (HDFS, Hive, Kafka, S3). · Working with batch and streaming ETL pipelines for data processing. Proficient with Git, Maven/Gradle, and DevOps tools. Expertise in debugging Spark transformations and ensuring performance. Preferred Qualifications: Hands-on experience converting PySpark workloads into Java Spark. Familiarity with ecosystems such as Databricks, Google Dataproc, or similar. Knowledge of Delta Lake or Apache Iceberg. Proven experience in big data performance modeling and tuning.

Responsibilities

The primary responsibility involves converting existing PySpark codebases into optimized Java-based Spark applications. This includes designing, developing, and maintaining scalable data processing solutions on platforms like Databricks.