Senior Lead Software Engineer, Big Data & Cloud Engineering — Risk

Build and operate data and analytics services that power critical markets risk decisions  this senior, hands-on role, you will lead complex engineering initiatives across a modern cloud and big data stack while shaping technical direction and standards. You will partner closely with stakeholders across Front Office, Risk, Product Control, and Finance to deliver timely, consistent, high-quality analytics. You will help strengthen reliability, governance, and performance across enterprise data platforms, including Databricks and Amazon Redshift.

As a Senior Lead Software Engineer at JPMorgan Chase in the Risk Central team, you will be a senior hands-on engineer and technical leader responsible for building and operating scalable data and analytics services supporting Markets risk use cases. You will lead delivery across ingestion, transformation, storage, and consumption layers, with a strong focus on operational excellence and predictable outcomes. You will design and evolve data products and workflows that run across Databricks and Amazon Redshift, improving consistency, controls, and performance.

You will mentor engineers, drive engineering best practices, and communicate clearly on progress, risks, dependencies, and trade-offs.

• Lead end-to-end delivery of complex initiatives across ingestion, transformation, storage, and consumption layers.
• Drive engineering best practices including design reviews, code reviews, testing standards, CI/CD, and documentation.
• Mentor engineers and raise the bar on technical quality, ownership, and operational excellence.
• Design and build robust batch and streaming pipelines using distributed compute for high-volume workloads.
• Implement efficient data modeling, partitioning, and performance tuning strategies for large datasets.
• Build reusable frameworks and components to accelerate onboarding of new datasets and analytics use cases.
• Engineer data products and workflows spanning Databricks and Amazon Redshift, including ingestion patterns, transformations, and serving layers.
• Define approaches to reconciliation, consistency, lineage, and controls across both enterprise data warehouses.
• Optimize query performance and cost across platforms, and establish best practices for workload placement.
• Build cloud-native solutions on AWS aligned to team standards, including services such as S3, EMR, Lambda, Kinesis or MSK, Glue, Event Bridge, DynamoDB, Redshift, and EKS.
• Own production stability by improving monitoring and alerting, incident management, root-cause analysis, preventative engineering, and SLAs and SLOs.

• Demonstrated experience delivering production software systems at scale.
• Proficiency in Python and or Java or Scala, with computer science fundamentals including data structures, algorithms, and object-oriented design.
• Hands-on experience with distributed data processing, such as Spark, and building data pipelines in batch and or streaming patterns.
• Experience with Databricks, Amazon Redshift, or equivalent enterprise data warehouse and lakehouse platforms, including designing and tuning performant workloads.
• Proficiency in SQL and understanding of data modeling and analytics patterns.
• Practical knowledge of cloud engineering concepts, including security, networking basics, IAM and access controls, encryption, and observability.
• Proven ability to troubleshoot production issues and drive operational improvements.
• Strong communication skills and experience working with globally distributed teams.

• Experience in Markets technology, including familiarity with the trade lifecycle, market risk measures, sensitivities, and P&L explain, plus common products…