Senior Java Developer, Credit Risk & Data Engineering

Salary: $150, per year

• 8+ years of professional experience in software engineering, data engineering, or application development, preferably in Financial Services, Capital Markets, Risk, or another complex enterprise setting.
• Strong hands-on Java programming skills, with working knowledge of Python and/or Scala for data-intensive application development.
• Production experience with Apache Spark, including building and optimizing large-scale distributed processing workloads.
• Experience with Parquet and large columnar datasets, including partitioning, schema management, compression, encoding, and performance optimization.
• Strong SQL skills and experience querying, transforming, reconciling, and analyzing large datasets.
• Experience designing scalable data pipelines, transformation frameworks, aggregation processes, and batch or distributed processing solutions.
• Knowledge of distributed computing, concurrency, software design, data structures, algorithms, and application architecture.
• Experience tuning data workloads, including Spark joins, partitioning, caching, data skew, resource utilization, and throughput.
• Experience building and integrating backend services, APIs, and enterprise application components.
• Experience across the software development lifecycle, from requirements and design through development, testing, deployment, and production support.
• Strong debugging, performance optimization, data-quality analysis, and problem-solving abilities.
• Experience working with business, risk, quantitative, and technology stakeholders and translating complex requirements into technical solutions.
• Strong written and verbal communication skills suited to a client-facing consulting environment.
• Bachelors degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent professional experience.
• Preferred:
  Experience supporting Credit Risk, Market Risk, regulatory risk, or enterprise trade and risk data platforms.
• Preferred:
  Familiarity with financial data such as trades, positions, sensitivities, reference data, counter party or exposure data, RWA, and VaR.
• Preferred:
  Experience with Big Data technologies such as Hive, HDFS, Iceberg, HBase, Impala, Kafka, Dremio, or EMR.
• Preferred:
  Experience with batch and streaming data processing, including real-time and historical financial data.
• Preferred:
  Experience with Java, Python, Scala, Spark/PySpark, SQL, Spring Boot, and modern backend frameworks.
• Preferred:
  Experience with cloud data platforms, particularly AWS and EMR, as well as containerization and modern CI/CD practices.
• Preferred:
  Performance engineering across Spark, Java, SQL, and large-scale storage platforms.
• Preferred:
  Experience modernizing, refactoring, or reverse engineering legacy technology and data platforms.
• Preferred:
  Experience with data quality, reconciliation, lineage, metadata management, and auditability in regulated environments.
• Preferred:
  Experience at Tier 1 investment banks or other large financial institutions, and prior management consulting experience.
• Preferred:
  Exposure to AI/ML techniques or agentic engineering tools used in data or software engineering.
• Preferred:
  Ability to work independently and own complex technical challenges from analysis through implementation.

• Design, develop, enhance, and support scalable software and data-processing solutions for Capital Markets, Credit Risk, Market Risk, and related Financial Services functions.
• Manage development across the full software lifecycle, from requirements analysis and technical design through implementation, testing, deployment, and production support.
• Build and optimize large-scale batch and, where needed, streaming pipelines using Apache Spark and related distributed technologies.
• Develop scalable backend services, processing engines, APIs, and integrations using Java, Python, Scala, or comparable technologies.
• Ingest, transform, enrich, aggregate, reconcile, and analyze trade, position, sensitivity, reference, counter party, and risk data.
• Apply effective partitioning, compression, encoding, compaction, and data-access strategies to Parquet and other columnar or table storage formats.
• Tune Spark workloads using methods such as repartitioning, salting, broadcast joins, and caching.
• Partner with Credit Risk, Market Risk, front-office, quantitative, data, architecture, and technology stakeholders to understand needs and develop solutions.
• Convert complex risk, business, and data requirements into robust technical…