Lead Software Engineer - Market Risk

Testing Site - JPMC; Jersey City (NJ); On-site; USD 140,000 - 210,000; Full time

Job summary

JPMorgan Chase seeks a Lead Software Engineer to join the Market Risk MXL DataLake Team. You will design and implement high-volume data pipelines and historical data stores, partnering with architects, risk technologists, and product owners to push the boundaries of data platforms.

You will lead production-grade data engineering efforts, optimize Spark workloads, and advance platform standards while ensuring compliance with regulatory requirements.

Qualifications

• Degree-level education in Computer Science, Software Engineering, or related discipline.
• Strong software engineering fundamentals including data structures and system design.
• Proven experience building large-scale data engineering solutions on big-data platforms.
• Hands-on 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.

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) 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.

Skills

PySpark / Spark

Data engineering

Distributed systems concepts

Data modelling

Big data platforms

Education

Degree-level education in Computer Science / Software Engineering

Tools

Databricks

Delta Lake

Job description

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.