About the role
structured by ORILead cloud data platforms powering markets risk analytics with modern engineering on AWS, Databricks, and Redshift. Build and operate data and analytics services that power critical markets risk decisions at scale.
What you will do
- 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.
What they are looking for
- 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.
Nice to have
- Experience in Markets technology, including familiarity with the trade lifecycle, market risk measures, sensitivities, and P&L explain, plus common products such as FX, rates, credit, and equities.
- Experience with streaming technologies and near-real-time analytics patterns, including Kafka, MSK, or Kinesis.
- Experience with governance, controls, and data quality frameworks including reconciliations, lineage, auditability, and entitlements.
- Familiarity with lake and lakehouse table formats and concepts, including Iceberg or Delta, and columnar storage formats such as Parquet.
- Experience with CI/CD, infrastructure as code, containerized workloads, and DevOps practices.
- Experience leading initiatives across multiple teams, including technical leadership, mentoring, and cross-team coordination.
Full posting text
Lead cloud data platforms powering markets risk analytics with modern engineering on AWS, Databricks, and Redshift.
Build and operate data and analytics services that power critical markets risk decisions at scale. In 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.
Job Responsibilities
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, EventBridge, DynamoDB, Redshift, and EKS.
Own production stability by improving monitoring and alerting, incident management, root-cause analysis, preventative engineering, and SLAs and SLOs.
Required Qualifications, Capabilities, and Skills
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.
Preferred Qualifications, Capabilities, and Skills
Experience in Markets technology, including familiarity with the trade lifecycle, market risk measures, sensitivities, and P&L explain, plus common products such as FX, rates, credit, and equities.
Experience with streaming technologies and near-real-time analytics patterns, including Kafka, MSK, or Kinesis.
Experience with governance, controls, and data quality frameworks including reconciliations, lineage, auditability, and entitlements.
Familiarity with lake and lakehouse table formats and concepts, including Iceberg or Delta, and columnar storage formats such as Parquet.
Experience with CI/CD, infrastructure as code, containerized workloads, and DevOps practices.
Experience leading initiatives across multiple teams, including technical leadership, mentoring, and cross-team coordination.
Risk Central is a strategic technology group within JPMorganChase’s Commercial and Investment Bank that builds and operates a next-generation analytics platform used for critical front office and risk reporting. The platform delivers timely, consistent, and high-quality data and analytics to support risk management and decision-making across global markets. A key aspect of the role is operating across two enterprise data warehouses, Databricks and Amazon Redshift, ensuring data consistency, performance, governance, and reliability across both platforms.
The role is aligned to the Risk Central team in London.