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# Java, Python Lead Software Engineering - Risk Data Platform & Strategy

- **Company:** JPMorgan Chase & Co.
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Agile Methodology, Artificial Intelligence, Airflow, Software Applications, Automation of Tests, Big Data, C Sharp (Programming Language), C++ (Programming Language), Cloud Computing, Cloud Engineering, Software Quality, Code Review, Continuous Delivery, Continuous Integration, Information Engineering, Data Systems, Database Queries, Software Debugging, Python (Programming Language), Machine Learning, Memcached, NoSQL, Systems Development Life Cycle, Raw Data, Redis, Software Tools, Secure Coding, Software Engineering, Software Systems, Workflow Management Systems, Snowflake, Grafana, Apache Spark, Data Lakes, Pyspark, Information Technology, Production Code, Maintaining Code, Apache Kafka, Api Design, Splunk, Code Restructuring, Dynatrace, Databricks, Programming Languages, Microservices
- **Published:** September 14, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/java-python-lead-software-engineering-risk-data-platform-strategy/47121331

## About the Role

the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale. Drive communities of practice across Software Engineering to promote new and leading-edge technologies Foster a team culture of diversity, opportunity, inclusion, and respect Required Qualifications, Capabilities, and Skills: Proficiency in Engineering & Architecture, AI/ML, with hands-on experience designing, implementing, testing, and ensuring operational stability of large-scale enterprise data platforms Advanced skills in one or more programming languages such as Java, Python, C/C++, or C# Practical experience delivering system design, application development, testing, and operational stability Working knowledge of relational and NoSQL databases and data lake architectures Experience developing, debugging, and maintaining code with modern programming languages and database querying languages Experience in
large-scale data processing, microservices, API design, Kafka, Redis, MemCached, Observability tools (Dynatrace, Splunk, Grafana), and Orchestration tools (Airflow, Temporal) Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls. Proficiency in automation, continuous delivery methods, and all aspects of the Software Development Life Cycle Advanced understanding of agile methodologies, CI/CD, application resiliency, and security Practical cloud-native
experience Preferred Qualifications, Capabilities, and Skills: Experience with modern data technologies such as Databricks or Snowflake Hands-on experience with Spark/PySpark and other big data processing technologies Demonstrated proficiency in software applications and technical processes within disciplines such as data engineering, cloud, artificial intelligence, machine learning, or mobile Knowledge of the financial services industry and their IT systems #J-18808-Ljbffr

## Description

impact of our technology solutions. Job Responsibilities: Execute creative software solutions, design, development, and technical troubleshooting to solve complex problems Develop secure, high-quality production code for data-intensive applications and review code written by others Identify opportunities to automate remediation of recurring issues and improve operational stability Lead evaluation sessions with external vendors, startups, and internal teams to assess architectural designs and technical credentials Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. Applies knowledge of tools within


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