Join us to shape the future of financial analytics and technology. You will lead impactful projects that drive advanced data engineering and AI solutions across global teams. We offer opportunities for career growth, collaboration, and technical excellence. Your expertise will help us deliver mission-critical systems and foster a culture of innovation. Be part of a team where your ideas and leadership make a difference.
As a Lead Data Engineering & AI Technical Initiatives in the Global Analytics team, you will guide technical direction and drive innovation in real-time data processing. You will collaborate with research and trading teams to deliver advanced analytics capabilities and support global business needs. Your role will focus on building robust tools, mentoring team members, and influencing product design. You will help shape our technical standards and foster a diverse, inclusive, and collaborative environment.
Job Responsibilities:
- Lead technical initiatives across global analytics teams, providing guidance and direction in a high-velocity environment.
- Design, build, and optimize real-time data processing pipelines and applications to ensure reliability and performance.
- Leverage AI technologies to enhance data engineering workflows and automate SDLC processes.
- Collaborate with research and trading teams to onboard new datasets efficiently and consistently.
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- 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.
- Build and support robust tools and frameworks for quantitative research and production trading.
- Mentor and develop team members, manage book of work, and drive continuous improvement in SDLC, testing, and coding standards.
- Influence product design, application functionality, and technical operations to meet evolving business demands.
- Serve as a subject matter expert in Python, KDB/Q, data engineering, and AI.
- Champion diversity, inclusion, and collaboration within global teams.
Required Qualifications, Capabilities, and Skills:
- Applied experience in software engineering, preferably in large-scale, fast-paced financial environments.
- Hands-on experience delivering system design, application development, testing, and operational stability for analytics-driven teams.
- Expertise in Python, KDB, or C++ for real-time data processing, application development, or data engineering.
- Working knowledge of AI technologies to support data engineering, analytics, or SDLC automation.
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Proficiency in automation and continuous delivery methods; advanced understanding of agile methodologies.
- Experience leading and mentoring teams in a global, collaborative environment.
- Ability to tackle complex design and functionality problems independently and drive solutions across distributed teams.
- Academic background in Computer Science, Computer Engineering, Mathematics, or a related technical field.
Preferred Qualifications, Capabilities, and Skills:
- Experience with market data venue and vendor data platforms.
- AWS experience; practical cloud native/cloud experience is a plus.
- Experience with Terraform and Kubernetes for managing production environments in public cloud.
- Knowledge and experience in FIX, Market Data, Analytics, OMS, and equities trading