Senior Lead Software Engineer, Big Data & Cloud Engineering — Risk Central (London)

As a Senior Lead Software Engineer in the Risk Central (London) team, you will be a senior hands-on engineer and technical leader responsible for building and operating scalable data and analytics services that support Markets risk use cases. You will work across a modern cloud and big data stack and partner with stakeholders across Front Office, Risk, Product Control, and Finance.
Risk Central is a strategic technology group within JPMorganChase’s CIB 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.
Job responsibilities

• Lead end-to-end delivery of complex initiatives across ingestion, transformation, storage, and consumption layers.
• Drive engineering best practices (design reviews, code reviews, testing standards, CI/CD, documentation).
• Mentor engineers and raise the bar on technical quality, ownership, and operational excellence.
• Design and build robust batch and streaming pipelines (high-volume, high-throughput) using distributed compute.
• Implement efficient data modeling, partitioning, and performance tuning strategies for large datasets.
• Build reusable frameworks/components to accelerate onboarding of new datasets and analytics use cases.
• Engineer data products and workflows that span Databricks and Redshift, including ingestion patterns, transformations, and serving layers.
• Define approaches to reconciliation/consistency, lineage, and controls across both warehouses.
• Optimize query performance and cost across platforms; establish best practices for workload placement.
• Build cloud-native solutions on AWS (e.g., S3, EMR, Lambda, Kinesis/MSK, Glue, EventBridge, DynamoDB, Redshift, EKS—depending on team standards).
• Leverage Spark-based processing (including PySpark/Scala Spark) and modern lake/lakehouse patterns where applicable.
• Own production stability: monitoring/alerting, incident management, root-cause analysis, and preventative engineering.
• Define and improve SLAs/SLOs for critical data deliveries and platform uptime.
• Stakeholder partnership - work closely with product managers, quants, risk managers, traders, and controllers to translate needs into scalable technical solutions.
• Communicate clearly with senior stakeholders on progress, risks, dependencies, and trade-offs.
• Contribute to planning, refinement, execution, and retrospectives; help teams deliver predictably with high quality.

Required qualifications, capabilities, and skills

• Extensive professional software engineering experience, including delivery of production systems at scale.
• Strong programming skills in Python (and/or Java/Scala), with strong CS fundamentals (data structures, algorithms, OO design).
• Hands-on experience with distributed data processing (e.g., Spark) and building data pipelines (batch and/or streaming).
• Experience with at least one of: Databricks, Amazon Redshift, or equivalent enterprise data warehouse/lakehouse platforms; ability to design and tune performant workloads.
• Strong SQL skills and understanding of data modeling and analytics patterns.
• Practical experience with cloud engineering concepts (security, networking basics, IAM/access controls, encryption, observability).
• Proven ability to troubleshoot production issues and drive operational improvements.
• Strong communication skills and experience working with globally distributed teams.

Preferred Qualifications

• Experience in Markets technology and familiarity with the trade lifecycle, risk concepts (market risk measures, sensitivities, P&L explain), and common products (FX, rates, credit, equities; derivatives basics).
• Streaming technologies (Kafka/MSK/Kinesis) and near-real-time analytics patterns.
• Experience with governance, controls, and data quality frameworks (reconciliations, lineage, auditability, entitlements).
• Familiarity with lake/lakehouse table formats and tooling (e.g., Iceberg/Delta concepts) and columnar storage formats (e.g., Parquet).
• CI/CD, infrastructure-as-code, containerized workloads, and DevOps practices.
• Prior experience leading initiatives across multiple teams (tech lead responsibilities, mentoring, cross-team coordination).