Director of Software Engineering - Big Data, Cloud

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    "ExternalDescriptionStr": "<p>We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.</p><p>&nbsp;</p><p>As a <strong>Director of Software Engineering</strong>&nbsp;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.</p><p><strong>Job responsibilities</strong></p><ul style=\"list-style-type: disc;\"><li>Leads the delivery of Trade Surveillance solutions aligned with business goals and regulatory requirements</li><li>Drives the platform modernization initiative, leveraging Data Mesh principles on Databricks to build scalable, resilient, and efficient big data platforms</li><li>Provides hands-on technical leadership and guidance to engineering teams, ensuring engineering excellence, secure-by-design, and agile/DevOps best practices</li><li>Manages multiple feature teams, acting as both a people supervisor and technical leader; hire, develop, mentor, and recognise talent across cross-functional teams</li><li>Owns decisions on team resources, budgets, and tactical operations, including process execution and continuous optimisation.</li><li>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.</li><li>Builds and nurture a collaborative environment that encourages innovation and continuous improvement.</li><li>Identifies and mitigate risks across data, models, security, and operations; ensure audit readiness and control compliance.</li><li>Develops strong partnerships with product, compliance, and LOB Technology also drives transformation and modernisation projects to achieve shared objectives.</li><li>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</li><li>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</li></ul><p>&nbsp;</p><p>&nbsp;</p><p><strong>Required qualifications, capabilities, and skills</strong></p><ul style=\"list-style-type: disc;\"><li>Solid track record and 10+ years of experience in building large-scale data warehouses, enterprise data platforms, and critical big data solutions</li><li>Proven experience in cloud implementation and practical cloud-native development with AWS, Azure, or GCP</li><li>Deep expertise in platform building, modernization, and transformation projects, including Data Mesh and Databricks</li><li>Familiarity with AI/ML technologies and experience leading or driving AI projects</li><li>Experience developing or leading cross-functional teams of technologists, including hiring, developing, and recognising talent</li><li>Strong people management skills; ability to supervise, mentor, and grow engineering leaders and teams</li><li>Excellent communication, collaboration, and stakeholder management skills</li><li>Ability to drive innovation and foster a culture of continuous improvement</li><li>Experience managing multiple Scrum/feature teams and delivering complex projects in an agile environment</li><li>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</li><li>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</li></ul><p>&nbsp;</p><p><strong>Preferred qualifications, capabilities, and skills</strong></p><ul><li>Experience working at code level</li></ul>",
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