Director of Software Engineering - Big Data and Cloud Platforms

Director of Software Engineering - Big Data, Cloud

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

 

As a Director of Software Engineering at JPMorgan Chase within Corporate Technology, you lead a technical area and drive impact within teams, technologies, and projects across departments. Utilize your in-depth knowledge of software, applications, technical processes, and product management to drive multiple complex projects and initiatives, while serving as a primary decision maker for your teams and be a driver of innovation and solution delivery.

Job responsibilities

• Leads the delivery of Trade Surveillance solutions aligned with business goals and regulatory requirements
• Drives the platform modernization initiative, leveraging Data Mesh principles on Databricks to build scalable, resilient, and efficient big data platforms
• Provides hands-on technical leadership and guidance to engineering teams, ensuring engineering excellence, secure-by-design, and agile/DevOps best practices
• Manages multiple feature teams, acting as both a people supervisor and technical leader; hire, develop, mentor, and recognise talent across cross-functional teams
• Owns decisions on team resources, budgets, and tactical operations, including process execution and continuous optimisation.
• Evaluates, selects, and integrates emerging technologies—including AI/ML solutions—to enhance surveillance coverage, accuracy, efficiency and also leads and drives AI projects, fostering AI readiness and adoption with the global team.
• Builds and nurture a collaborative environment that encourages innovation and continuous improvement.
• Identifies and mitigate risks across data, models, security, and operations; ensure audit readiness and control compliance.
• Develops strong partnerships with product, compliance, and LOB Technology also drives transformation and modernisation projects to achieve shared objectives.
• Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernisation, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, 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 realised by automation and support capacity unlock initiatives at scale

 

 

Required qualifications, capabilities, and skills

• Solid track record and 10+ years of experience in building large-scale data warehouses, enterprise data platforms, and critical big data solutions
• Proven experience in cloud implementation and practical cloud-native development with AWS, Azure, or GCP
• Deep expertise in platform building, modernization, and transformation projects, including Data Mesh and Databricks
• Familiarity with AI/ML technologies and experience leading or driving AI projects
• Experience developing or leading cross-functional teams of technologists, including hiring, developing, and recognising talent
• Strong people management skills; ability to supervise, mentor, and grow engineering leaders and teams
• Excellent communication, collaboration, and stakeholder management skills
• Ability to drive innovation and foster a culture of continuous improvement
• Experience managing multiple Scrum/feature teams and delivering complex projects in an agile environment
• Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorised tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs
• Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse

 

Preferred qualifications, capabilities, and skills

• Experience working at code level

Experience Level

Executive Level