Senior Principal - Databricks Platform Engineering Lead (Python)

JPMorgan Chase & Co.•Jersey City, NJ

3d•$232,750 - $325,000

About The Position

Join JPMorgan Chase as a Senior Principal Software Engineer where you will own the Databricks platform architecture on AWS, build Terraform/Python automation, mentor teams, and drive high-impact data innovation. 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 Senior Principal Software Engineer at JPMorganChase in Corporate – AIML Data Platforms and the Chief Data & Analytics team, you will bring your deep expertise in Databricks and large-scale data engineering. In this role you will drive the architecture, design, and implementation of advanced data solutions, leveraging Databricks and related technologies to enable business insights and innovation.

Requirements

Formal training or certification on software engineering concepts and 10+ years applied experience
Expert-level proficiency in Databricks platform administration, AWS networking, and infrastructure-as-code with Terraform
Hands-on experience with Python application program development with use of automated unit testing
Hands-on practical experience delivering system design, application development, testing, and operational stability
Hands-on practical experience with terraform development and understanding of terraform enterprise
Hands-on experience with GitHub / Bitbucket code versioning tool, Jenkins build tool and pypi / maven artifactory integrations
Experience in managing product release lifecycle at enterprise level.
Demonstrated experience designing and scaling agentic AI-enabled development patterns (using enterprise-authorized tools within the work environment) across teams/functions, including establishing governance for human-in-the-loop validation, traceability/auditability, and secure handling of sensitive inputs/outputs.
Strong understanding of responsible AI use and control expectations at scale, including security/resiliency implications, data sensitivity, and risk-based governance; ability to advise senior leaders on safe adoption, reuse, and measurable outcomes.

Nice To Haves

Strong knowledge of distributed compute frameworks like Spark and how platform-level configurations impact workload performance and reliability
Experience with Agile development processes, as needed (SCRUM/KANBAN) using JIRA.
Experience in Data pipelines using Spark

Responsibilities

Leads the design, build, and troubleshooting of a scalable Databricks/Spark data platform and analytics workloads across cloud environments.
Develops and maintains secure, production-grade Python code; perform code reviews, debugging, and engineering quality improvements across the team.
Owns delivery of scalable data pipelines and analytics solutions on Databricks, applying strong distributed computing and performance troubleshooting practices.
Designs and develops infrastructure automation for the Databricks control plane, including workspace provisioning, account configuration, onboarding/offboarding, and Delta Sharing using Terraform, AWS, and Python Lambdas.
Develops secure, high-quality production code across Terraform modules, Python Lambda functions, and control plane services while reviewing and debugging code across the team.
Leads the architecture of scalable Databricks platform infrastructure, including workspace automation, AWS networking, and control plane APIs enabling self-service for downstream teams.
Solves the companies most challenging cloud data platform problems by building innovative technical solutions around Data Lake Tools.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Mentor and guide technical teams, fostering a culture of continuous learning and excellence in software engineering practices
Sets strategy and operating standards for agentic AI-enabled engineering across a portfolio (using enterprise-authorized tools within the work environment) to drive measurable improvements in delivery speed, reliability, and code quality (e.g., AI-orchestrated SDLC/TLM automation, release readiness gating, incident triage/root-cause acceleration, and large-scale refactoring/test modernization), while defining guardrails for validation, security, resiliency, and reuse across teams and functions.
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.