Senior Lead Software Engineer Risk Technology Data Platform & Strategy

Be an integral part of an agile Engineering & Architecture team thats constantly pushing the envelope to enhance build and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorganChase within the Corporate Risk Technology you are an integral part of an agile team that works to design build enhance and deliver advanced data engineering solutions and trusted market-leading technology products in a secure stable and scalable way.

Job responsibilities

• Lead the design and development of secure high-quality production code for data-intensive applications; review and mentor other engineers

• Regularly provides technical guidance and direction to support the business and its technical teams contractors and vendors

• Drives decisions that influence the product design application functionality and technical operations and processes

• Serves as a function-wide subject matter expert in one or more areas of focus

• Actively contributes to the engineering community as an advocate of firmwide frameworks tools and practices of the SoftwareDevelopment Life Cycle

• Influences peers and project decision-makers to consider the use and application of leading-edge technologies

• Influences leaders and stakeholders across business product and technology teams

• Adds to the team culture of opportunity inclusion and respect

• 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 the Software Development Life Cycle toolchain including approved AI-assisted development and automation capabilities to improve the value realized by automation at scale.

Required qualifications capabilities and skills

• Formal training or certification on software engineering concepts and 5 years applied experience

• Strong proficiency in Engineering Architecture AI/ML with hands-on experience in designing implementing testing and ensuring the operational stability of large-scale enterprise data platforms and solutions

• Hands-on practical experience delivering system design application development testing and operational stability

• Advanced in one or more programming language(s) eg. Java Python C/C

• Advanced Working knowledge of Databases/Data Lake/Data Mesh and Data governance.

• Experience developing debugging and maintaining code in a large corporate environment with expertise in both application and data platforms using modern programming and database querying languages.

• Experience in large scale data processing using micro services API design Kafka Redis MemCached Observability (Dynatrace Splunk Grafana or similar) Orchestration (Airflow Temporal)

• Practical cloud native experience (AWS Azure GCP).

• Experience in Computer Science Computer Engineering Mathematics or a related technical field

• 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.

Preferred qualifications capabilities and skills

• Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g. data engineering cloud artificial intelligence machine learning etc.)

• Experience with modern data technologies such as Databricks or Snowflake.

• Knowledge of the financial services industry and their IT systems