Lead Software Engineer - Market Risk
at J.P. Morgan
Posted 14 days ago
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Lead Software Engineer on the Market Risk MXL DataLake Team building cutting-edge data platforms for market risk and analytics. You will design and implement large-scale historical data stores and robust, scalable PySpark/Spark data pipelines for batch and incremental processing while applying strong data-modeling principles to support long-term historical and regulatory needs. The role emphasizes production-grade engineering—performance optimization, testability, maintainability—and close collaboration with architects, risk technologists, and product owners to evolve platform standards and best practices.
- Compensation
- Not specified
- City
- Jersey City
- Country
- United States
Currency: Not specified
Full Job Description
Location: Jersey City, NJ, United States
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorgan Chase within the Market Risk MXL DataLake Team, you will join a strategic initiative building cutting-edge data platforms for market risk and analytics. In this role, you'll design and implement high-volume data pipelines and historical data stores, collaborating closely with architects, risk technologists, and product owners.
Job Responsibilities
- Design, build, and maintain large-scale historical data stores on modern big-data platforms
- Develop robust, scalable data pipelines using PySpark / Spark for batch and incremental processing
- Apply strong data-modelling principles (e.g. dimensional, Data Vault–style, or similar approaches) to support long-term historical analysis and regulatory requirements
- Engineer high-quality, production-grade code with a focus on correctness, performance, testability, and maintainability
- Optimize Spark workloads for performance and cost efficiency (partitioning, clustering, file layout, etc.)
- Collaborate with architects and senior engineers to evolve platform standards, patterns, and best practices
- Contribute to code reviews, technical design discussions, and continuous improvement of engineering practices
Required qualifications, capabilities and skills
- Degree-level education in Computer Science, Software Engineering, or a related discipline (or equivalent practical experience)
- Strong software engineering fundamentals, including data structures, algorithms, and system design
- Proven experience building large-scale data engineering solutions on big-data platforms
- Hands-on experience developing PySpark / Spark pipelines in production environments
- Solid understanding of data modelling for analytical and historical data use cases
- Experience working with large volumes of structured data over long time horizons
- Familiarity with distributed systems concepts such as fault tolerance, parallelism, and idempotent processing.
Preferred Qualifications
- Experience with Databricks, Delta Lake, or similar cloud-based big-data platforms
- Hands-on experience designing and implementing Data Vault 2.0 models.
- Exposure to historical / regulatory data platforms, risk data, or financial services
- Knowledge of append-only data patterns, slowly changing dimensions, or event-driven data models
- Experience with CI/CD, automated testing, and production monitoring for data pipelines
- Experience building highly reliable, production-grade risk systems with robust controls and integration with modern SRE tooling.
