Lead Software Engineer - Market Risk

JPMorgan Chase & Co.•Jersey City, NJ

About The Position

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.

Requirements

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.

Nice To Haves

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.

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