Lead Software Engineer - Market Risk Reporting & Visualization

Job Description

We have an exciting opportunity for an experienced software engineer to shape the next generation of market risk reporting and analytics.

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 high-quality analytical datasets, reporting pipelines, and visualization-ready views using PySpark, SQL, and modern cloud data platforms.

You will work closely with risk managers, quantitative and data teams, product partners, and other engineers to turn complex business questions into clear, trusted, and useful reporting.

The team is advancing AI-enabled engineering practices to improve how it analyzes requirements, develops and tests pipelines, and delivers reporting capabilities.

You will help establish effective patterns for this work, including sound engineering controls and human review, while remaining accountable for the quality, security, and correctness of production outcomes.

Job Responsibilities


* Lead the design and delivery of scalable PySpark and SQL pipelines that transform complex 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 that support interactive reporting, visualization, analysis, and business decision-making.


* Partner with business stakeholders and risk subject-matter experts to clarify reporting needs, identify the right data, and translate requirements into maintainable technical solutions.


* Guide architecture and implementation across batch data processing, lakehouse storage, and visualization or reporting integration.


* Drive adoption of enterprise-authorized AI-assisted development practices, setting clear expectations for validating 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; 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 clearly to business partners, engineering peers, and senior leaders.

Required qualifications, capabilities, and skills
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