Job Title: Vice President – Senior KDB/Q Developer
Job ID/Reference Code: JR-0000056847
Location: Pune, India
Experience Required: 10+ Years
Educational Requirements:
Bachelor’s or Master’s Degree in Computer Science / Mathematics / Engineering / Physics or related quantitative discipline
Role Overview:
Join Barclays as a Vice President – Senior KDB/Q Developer, where you will play a critical role in shaping the development and support of ultra-low latency trading systems. You will lead efforts in building and maintaining large-scale KDB+/q-based time-series databases and analytics platforms that empower decision-making across our Markets division. This is a high-impact, front-office aligned role for a hands-on technologist with deep technical skills and strategic vision.
Key Responsibilities:
Architect, design, and develop KDB+/q applications supporting real-time and historical analytics for trading platforms.
Build scalable data pipelines and optimize storage/access strategies for high-frequency data.
Ensure performance, security, and availability of time-series databases across multiple environments.
Collaborate closely with traders, quants, and developers across regions to deliver robust and innovative solutions.
Drive best practices in SDLC, Agile methodologies, DevOps adoption, and deployment automation.
Contribute to technical strategy, innovation, and architecture decisions across the team.
Mentor junior developers and foster a culture of knowledge sharing and excellence.
Ensure risk and control compliance through secure coding, governance, and regulatory alignment.
Technical Requirements:
Essential Skills:
Strong hands-on expertise in KDB+/q programming on Linux environments
Proven experience developing front-office / electronic trading systems
Proficiency in Java, Maven, Git, TeamCity, JIRA, and Confluence
Experience with Agile methodologies and full software development lifecycle
Strong data architecture and analytical skills focused on high-performance systems
Preferred Skills:
Prior experience designing large-scale distributed KDB+ systems
Exposure to machine learning pipelines and integration with data science platforms
Good academic record with a focus on numerate disciplines
Experience in low-latency trading or tick data analysis environments
