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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Software Engineer - Databricks - **Company:** JPMorgan Chase & Co. - **Location:** Jersey City, NJ, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Software Quality, Data Governance, Data Warehousing, Distributed Computing Environment, Identity and Access Management, Python (Programming Language), Metadata, Performance Tuning, Systems Development Life Cycle, Software Engineering, Data Lakes, Production Code, Virtual Agents, Terraform, Databricks - **Published:** August 29, 2026 - **Apply:** https://jpmc.fa.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/requisitions/preview/210781975 ## About the Role * 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. Preferred qualifications, capabilities, and skills * 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. ## Description * 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. ## Related Videos - [Blueprints for Success: Steering a Global Data & AI Architecture](https://www.wearedevelopers.com/videos/1577-blueprints-for-success-steering-a-global-data-ai-architecture) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) ## Related Articles - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk)