Software Engineer III – Market Risk Reporting & Visualization

Design, build, test, and run PySpark/SQL solutions that transform complex risk datasets into reliable reporting and analytics for market risk.

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

As a Software Engineer III 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

Design, build, and maintain production PySpark and SQL pipelines for market risk reporting and analytics.

Transform complex source data into well-defined, reusable datasets and views for dashboards, reports, and analytical applications.

Work with business partners and risk subject-matter experts to understand requirements, trace data needs, and deliver fit-for-purpose solutions.

Apply data modeling, data quality, lineage, and governance practices so reporting outputs are consistent, traceable, and trusted.

Develop secure, readable, and maintainable code; participate in design discussions, code reviews, and continuous improvement.

Build and maintain automated tests and deployment workflows, and support monitoring and operational stability of data products.

Investigate data discrepancies, performance bottlenecks, and production issues, and implement durable fixes.

Use enterprise-authorized AI-assisted engineering tools to accelerate coding, testing, documentation, and troubleshooting while checking results for correctness, security, and performance.

Explore practical uses of AI and automation in reporting and analysis, incorporating appropriate validation and human review.

Collaborate effectively with other engineers, business stakeholders, and platform teams to deliver work in an agile environment.

Required qualifications, capabilities, and skills

Bachelor’s degree in computer science, data science, engineering, applied mathematics, or a related technical discipline, or equivalent practical experience.

Strong professional experience developing and supporting production software or data engineering solutions.

Strong Python and PySpark skills, with solid SQL and understanding of distributed processing concepts.

Experience building data pipelines on a cloud data platform or lakehouse, working with large datasets and cloud object storage.

Knowledge of data modeling, data quality, and the design of reliable datasets for analytics and reporting.

Experience with software development fundamentals, including version control, testing, code review, CI/CD, and production support.

Ability to analyze business and technical requirements and turn them into clear, maintainable implementations.

Experience using approved AI-assisted development tools, with care to review and test generated output rather than relying on it without validation.

Strong problem-solving, communication, and teamwork skills.

Interest in financial markets, market risk, or other complex data domains.

Preferred qualifications, capabilities, and skills

Experience with Databricks, Apache Spark, Delta Lake, Unity Catalog, and Amazon Web Services.

Experience preparing data for dashboards, visualization tools, regulatory reporting, or analytical applications.

Familiarity with data governance, lineage, data observability, and data product practices.

Exposure to LLMs, agentic AI, or automation techniques applied to software development or data workflows.

Experience with APIs or services for accessing analytical data.

Knowledge of market risk concepts such as sensitivities, Value at Risk, stress testing, or limits.

Experience tuning Spark workloads and improving pipeline performance, reliability, or cost.