below marketmost similar roles pay hereabove market
This listing doesn't post a salary. Most similar roles pay
$142,450–$215,000.
Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1226 open roles
on FindRole.
Listed pay typically runs
$156,750–$215,000
across 5 roles with salary data.
As a Lead Software Engineer in the Market Risk Data Visualization Team, you will provide technical leadership to develop data products that help risk professionals navigate complex market risk environments. You will design and deliver scalable PySpark and SQL pipelines, transforming market risk data into trusted, analytics-ready datasets and visualization-ready views. Your daily work involves building data models, defining reusable engineering patterns like data contracts and lineage, and optimizing batch data processing on modern lakehouse platforms such as Databricks and Delta Lake. You will utilize Amazon S3 and cloud object storage while driving AI-assisted development practices for pipeline testing and requirements analysis. The role requires translating business needs into technical solutions, ensuring data quality, security, and performance across large data workloads within a financial market context.
Design and deliver scalable PySpark and SQL pipelines to transform complex market risk data into analytics-ready datasets.
Define reusable engineering patterns for data products, including data contracts, semantic definitions, quality checks, and lineage.
Build and optimize data models and views to support interactive reporting and business decision-making.
Translate business requirements from risk experts into maintainable technical solutions and architecture designs.
Drive the adoption of AI-assisted development practices for pipeline development, testing, and requirements analysis.
Develop secure, production-quality code while leading code reviews and improving CI/CD and observability practices.
Identify and resolve performance, scalability, and data quality issues across large-scale data workloads.
Mentor engineers through technical coaching, design reviews, and pairing to raise overall engineering standards.
What we're looking for
Bachelor’s or advanced degree in computer science, data science, engineering, applied mathematics, or a related technical discipline, or equivalent practical experience.
Significant experience designing, building, and operating production data-intensive applications, including technical leadership of complex deliveries.
Expert-level Python and PySpark skills with strong SQL and a solid understanding of distributed data processing and Spark performance.
Hands-on experience with a modern lakehouse or cloud data platform, such as Databricks and Delta Lake, and cloud object storage such as Amazon S3.
Strong understanding of data modeling, analytical data products, data quality, lineage, access controls, and governance in an enterprise environment.
Experience designing data products for reporting, visualization, or analytical use, including translating business definitions into consistent, usable datasets.
Experience with automated testing, CI/CD, code review, production monitoring, and operational support for data pipelines.
Demonstrated experience using approved AI-assisted software development tools effectively, with disciplined validation of AI-generated code and recommendations.
Python
CI/CD
NoSQL
Agile
System Design
Distributed Systems
Real-time Data Processing
AI-assisted Engineering
Software Development Life Cycle
Application Resiliency
Security
SecDB
Quartz
Athena