Lead Software Engineer – Market Risk Reporting & Visualization

JPMorganChase•Jersey City, NJ

About The Position

As a Lead Software Engineer in the Market Risk Data Visualization Team, you will provide hands-on technical leadership for data products that help risk professionals understand a large, complex, and fast-moving market risk environment. You will design and deliver analytical datasets, reporting pipelines, and visualization-ready views using PySpark, SQL, and modern cloud data platforms. Working with risk managers, quantitative and data teams, product partners, and engineers, you will translate complex business questions into clear, trusted reporting. The team is advancing AI-enabled engineering practices to improve requirements analysis, pipeline development and testing, and reporting delivery. You will help establish effective patterns for this work, including sound engineering controls and human review, while remaining accountable for production quality, security, and correctness.

Requirements

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, 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.
Demonstrated ability to lead technical design, make pragmatic architecture decisions, and communicate trade-offs to varied audiences.
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.
Strong collaboration skills and ability to work with business partners and technical teams in an agile delivery environment.
Familiarity with financial markets, market risk, or other complex financial data domains, or ability to learn the domain quickly.

Nice To Haves

Experience with Databricks workflows or pipelines, Unity Catalog, Delta Lake, and cloud-native data services.
Experience with reporting and visualization tools or frameworks, and designing datasets for interactive dashboards and analytical applications.
Experience with data product or data mesh practices, including discoverability, ownership, reusable interfaces, and governed self-service access.
Experience applying LLMs or agentic techniques to software engineering, requirements analysis, data investigation, or reporting workflows.
Experience building APIs or services that make governed analytical data available to applications and AI-enabled tools.
Knowledge of market risk measures, sensitivities, Value at Risk, stress testing, limits, or related risk reporting.
Experience with infrastructure as code, data observability, and performance or cost optimization at scale.

Responsibilities

Lead the design and delivery of scalable PySpark and SQL pipelines that transform market risk data into trusted, analytics-ready datasets.
Define reusable engineering patterns for reporting data products, including data contracts, semantic definitions, quality checks, lineage, testing, deployment, and operational support.
Build and optimize data models and views for interactive reporting, visualization, analysis, and business decision-making.
Partner with business stakeholders and risk subject-matter experts to clarify reporting needs, identify data, and translate requirements into maintainable technical solutions.
Guide architecture and implementation across batch processing, lakehouse storage, and visualization or reporting integration.
Drive adoption of enterprise-authorized AI-assisted development practices, including validation of generated code and analysis for correctness, performance, security, and data handling.
Identify opportunities to apply AI and automation to requirements analysis, pipeline development, test generation, data quality investigation, and reporting workflows, with appropriate human oversight.
Develop secure, resilient, production-quality code; lead code reviews; and improve automated testing, CI/CD, observability, and support practices.
Mentor engineers through technical coaching, design reviews, pairing, and constructive feedback, and help raise engineering standards across the team.
Identify and resolve performance, scalability, data quality, and operational issues across large data workloads.
Communicate technical options, delivery risks, and recommendations to business partners, engineering peers, and senior leaders.