Senior Java Spark Developer - Credit Risk & Big Data; Financial Services

Core Java and Apache Spark Engineer

Required Qualifications /Skills/

Experience:

• 7+ years of experience in software development, specifically Core Java & Java 8 concepts (OOP, OOD, Collections, Exception Handling, JDBC, Multithreading, Streams, Lambda Expressions, Functional Interfaces, etc.).
• 5+ years of experience in SQL/No
  
  SQL database management systems (Oracle, MySQL, Postgres, MongoDB, Couchbase, Neo4j).
• 3+ years of experience leading/owning software development projects from beginning to end with multiple team members.
• 2+ years of experience in Apache Spark (Java API) with good knowledge of Apache Spark concepts (RDD, Data Frame, Dataset, etc.).
• Experience working in financial markets and credit risk enterprise software systems, including but not limited to scenario analysis and stress testing.
• Experience with Git Hub, Tekton, Harness, and other CI/CD pipeline technologies.
• Ability to work in a fast-paced Technology Finance environment.

Preferred Qualifications /Skills/

Experience:

• Experience with agentic AI tools (Devin, Claude, or Copilot).

Overview:

Seeking a Core Java and Apache Spark Engineer with expertise in big data processing, particularly within the Technology Finance industry. Candidate should have experience working with financial enterprise technologies and large-scale distributed computing systems. This role involves developing and optimizing data pipelines for credit risk calculations and regulatory reporting.

Job Duties:

• Design, develop, optimize, and maintain scalable data pipelines for processing and analyzing large-scale financial data built on Core Java and Apache Spark.
• Lead projects for developing batch pipeline processes for credit risk analytics, including but not limited to stress loss (GSST, CCAR) and expected credit loss (CECL, IFRS9, ICAAP).
• Ensure the efficient storage and retrieval of all risk data in big data platform systems (Hadoop, Hive, Impala, Spark, etc.).
• Implement best practices for Spark performance tuning, including but not limited to partitioning, caching, and memory management.
• Maintain code quality through high-efficiency testing, CI/CD pipelines (Tekton, Harness), and version control (Git Hub).

** Only those lawfully authorized to work in the designated country associated with the position will be considered.**

** Please note that all Position start dates and duration are estimates and may be reduced or lengthened based upon a client's business needs and requirements.**