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.
If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place.
The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.
As a Principal Software Engineer at JPMorganChase within the Chief Data Analytics Office - AIML Data Platforms Team, you provide expertise and engineering excellence as an integral part of an agile team to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various technologies to support one or more of the firm’s portfolios.
Job responsibilities
Creates complex and scalable coding frameworks using appropriate software design frameworks
Develops secure and high-quality production code, and reviews and debugs code written by others
Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Advises cross-functional teams on technological matters within domain of expertise
Serves as the function’s go-to subject matter expert
Contributes to the development of technical methods in specialized fields in line with the latest product development methodologies
Creates durable, reusable software frameworks that are leveraged across teams and functions
Influences leaders and senior stakeholders across business, product, and technology teams
Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
10+ years of experience (or equivalent expertise) leading technology organizations and delivering complex, enterprise-scale programs.
Proficient in Python and SDLC processes around it at an enterprise grade.
Proficient in AWS Infrastructure provisioning using Terraform and Hands-on experience in AWS resources focusing on Network boundaries, Resource and IAM policies, cross account / region access patterns and Compute at Scale.
Executive-level stakeholder leadership: demonstrated ability to influence and align senior leaders across business, product, and technology organizations.
Deep expertise in Databricks in large enterprises, including architecture, performance tuning, cost management, security patterns, and operational excellence.
Ability to manage multiple portfolios and competing priorities, establishing clarity, execution discipline, and transparent decision-making.
Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data.
Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.
Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data.
Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.
Strong understanding of global data governance, privacy, and regulatory requirements, with the ability to translate them into pragmatic technical architecture decisions and engineering requirements. Strong understanding of modern data platform architectures (data lakes, data warehouses, lakehouse) and distributed computing frameworks.
Proven experience with enterprise metadata, catalog, and lineage platforms, plus practical expertise with data contracts and schema governance.
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