Senior Lead Software Engineer - Risk Technology Data Platform & Strategy

Be an integral part of an agile Engineering & Architecture team that's 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 Software Development 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