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AI Data Engineer – Next-Generation Analytics & AI-Ready Data Platforms (KDB+)
About the Role
We’re seeking an AI Data Engineer to lead the construction and maintenance of cutting-edge data platforms powered by KDB+, ensuring critical support for trading, research, and risk use cases across our Markets division.
Key accountabilities include architecting, building, and optimising data pipelines, warehouses, and lakes, ensuring they handle durable, consistent, and secure data at scale—directly enabling AI-driven insights and real-time decision-making.
Primary Responsibilities
Core Technical Deliverables
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Data Pipeline & Architecture Design & Maintenance
- Develop, deploy, and optimise scalable, low-latency data pipelines to ensure efficient data ingestion, processing, and delivery for trading, risk, and analytical use cases.
- Implement data storage solutions (e.g., data warehouses, data lakes) that balance volume, velocity, and security while maintaining accuracy, completeness, and consistency.
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AI & Advanced Analytics Integration
- Collaborate with data scientists to integrate machine learning models and AI algorithms into production systems.
- Drive the evolution of AI-ready data platforms, translating cutting-edge AI capabilities into production-grade, high-performance solutions.
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Algorithm Development
- Design and implement sophisticated processing and analytical algorithms tailored to high-frequency, heterogeneous, or unstructured data.
Leadership & Strategic Influence
For Senior Roles (VP/Leadership Level)
- Strategic & Operational Leadership
- Contribute to or define data strategy, drive requirements, and recommend process improvements.
- Plan and manage budgets, policies, and resources; escalate compliance breaches and drive continuous enhancements.
- Define team structures, job roles, and career paths (if applicable) to align with future business needs.
- Counsel team members on performance, enlist in pay/bonus decisions, and ensure skilled succession planning.
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Leadership Behaviours (LEAD Framework for People Leaders)
- Listen and be authentic (L) – Foster psychological safety and open communication.
- Energise and inspire (E) – Motivate teams to deliver excellence.
- Align across the enterprise (A) – Ensure cross-functional cohesion with strategic goals.
- Develop others (D) – Nurture talent and coach high potential candidates.
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Risk & Governance
- Assess and mitigate risks while maintaining robust controls, security, and compliance standards.
- Advise stakeholders (executives, functional leads) on data-related decisions, ensuring alignment with business objectives.
For Individual Contributor Roles
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Technical Innovation & Mentorship
- Act as a subject matter expert (SME) in your discipline, guiding long-term architectural decisions.
- Lead multi-year assignments, combining structured methodologies with specialised domain knowledge.
- Train and coach junior/entry-level engineers, bridging gaps in AI, trading data, or low-latency architectures.
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Strategic Contribution
- Provide insights to inform organisational risks, long-term profitability, and strategic data initiatives.
Key Skill Requirements
Technical Expertise
- AI/Distributed Systems Experience
- Proven track record of impactful AI initiatives with quantifiable ROI.
- Experience at the forefront of technological evolution, rapidly adopting new paradigms (e.g., time-series data, edge computing,Scalable AI models).
- Practical understanding of challenges in integrating AI into low-latency, high-frequency trading environments.
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Front Office & Market Data Proficiency
- Strong familiarity with trading workflows, high-frequency market data dynamics, and operational complexities of real-time systems.
- Hands-on expertise with Python and KDB+ (≥3 years preferred).
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Data Systems & Infrastructure
- Deep knowledge of data warehousing (e.g., Snowflake, BigQuery), data lakes (Delta Lake, Iceberg), and scalable storage solutions.
- Expertise in ETL/ELT pipelines, real-time processing frameworks (e.g., Kafka, Flink, Spark), and low-latency architectures.
Soft Skills & Stakeholder Collaboration
- Risk & Controls Awareness
- Ability to assess and mitigate data-related risks (e.g., compliance, security, bias, latency risks).
- Critical Thinking & Problem-Solving
- Capacity to analyse complex alternatives, weigh trade-offs, and develop data-driven solutions.
- Stakeholder Management
- Build trusting relationships with internal/external stakeholders (e.g., traders, risk teams, finance, data science).
- Communicate effectively with diverse teams, translating technical jargon into actionable insights.
- Ethical Decision-Making
- Champion Barclays Values (Respect, Integrity, Service, Excellence, Stewardship) and Mindset (Empower, Challenge, Drive) in every decision.
Benefits
(Note: While not detailed here, typical benefits may include competitive salary, pension contributions, flexible working policies, professional development funds, and markets-specific perks.)
Location
- Primarily based in our London office.
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