JPMorgan Chase
From JPMorgan Chase's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Be an integral part of an agile 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 and Investment Bank (CIB) Payments Technology – Regulatory Reporting, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.
Job responsibilities
Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
Owns end-to-end technical delivery for Regulatory Reporting initiatives; remains hands-on as a senior individual contributor
Develops secure and high-quality production code, and reviews and debugs code written by others
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
Builds and optimizes data pipelines and reporting workloads using Databricks Lakehouse (Delta Lake, Spark SQL, PySpark)
Drives decisions that influence the product design, application functionality, and technical operations and processes
Partners with stakeholders to deliver controls for regulatory submissions (data quality, lineage, reconciliation, audit evidence)
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
Required qualifications, capabilities, and skills
Bachelor's Degree in Computer Science/ Information systems/ Engineering or related disciplines
Minimum of 8 years of hands-on software development experience delivering system design, application development, testing, and operational stability
Advanced in one or more programming language(s) (Python required)
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
Ability to tackle design and functionality problems independently with little to no oversight
Practical cloud native experience (AWS preferred)
Hands-on experience with PySpark/Apache Spark and Databricks (Delta Lake/Lakehouse)
Good exposure to Payments and Regulatory Reporting domains
Experience with agentic AI patterns for engineering automation and operational workflows (within approved environments)
Experience with data quality/reconciliation frameworks for regulatory reporting
Experience with CI/CD and automated testing for data pipelines
Exposure to AWS data services (e.g., S3, Glue, Lambda, Step Functions, EMR)
Exposure to observability tooling (logs/metrics/traces) for production support
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