Sr Lead Software Engineer – KDB+ / Q

Overview

Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank. You will be a core technical contributor in the Data Analytics team, building and refining KDB-based systems for real-time and historical market data. You’ll help migrate to AWS, re-architect applications, and drive greenfield projects with secure, scalable solutions. You will lead communities of practice and collaborate with stakeholders to deliver trusted, market-leading technology that aligns with the firm’s objectives. This role offers the opportunity to shape high-impact data platforms in a large, global institution.

Responsibilities

• Execute creative software solutions and troubleshoot complex problems beyond routine approaches
• Develop secure, production-grade code and review others’ code
• Identify opportunities to automate recurring issues to improve stability
• Lead communities of practice to promote new technologies
• Foster team culture of diversity, equity, inclusion, and respect
• Develop core systems and frameworks based on KDB
• Lead team members via book of work management and drive SDLC improvements
• Develop scalable real-time processing solutions using agile methodology
• Partner with stakeholders to capture requirements and deliver solutions
• Collaborate with application support teams to maintain and support the platform

Key requirements

• Formal training or certification on software engineering concepts
• Hands-on experience in system design, development, testing, and operational stability
• Proficiency in automation and continuous delivery methods
• Strong understanding of Software Development Life Cycle and agile methodologies (CI/CD, application resiliency, security)
• Development lead experience: requirements capture, task decomposition, estimation, delivery planning, testing, UAT
• Deep understanding of KDB and Q language; at least 7 years professional KDB experience; plus 2+ years in a lead role
• Experience with KDB+tick design and data organization; performance implications of approaches
• Experience handling large datasets, optimizing query performance, scaling and load-balancing KDB apps, and building resilient/HA KDB systems

• Cross-functional collaboration
• Stakeholder partnership
• Mentorship and leadership
• KDB
• Q language
• AWS

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