Senior GenAI Data VP – Market Data

Embark on a transformative journey as a Senior GenAI Data VP – Market Data  vision is to redefine the future of banking by responsibly harnessing data and Generative AI to deliver innovative, scalable, and trusted solutions across Markets and Investment Banking. In this role, you will lead the strategy, design, delivery, and adoption of enterprise‑grade GenAI data platforms, with a focus on market data.

You will own and deploy agentic GenAI data workflows, spanning retrieval‑augmented generation (RAG), tool calling, and orchestration, while embedding governance, controls, and data quality from the outset. You will architect and scale AI agent capabilities across Azure and AWS, leveraging Microsoft 365 Copilot and Copilot Studio, and integrating leading OpenAI and Anthropic LLMs with built‑in privacy, security, and regulatory safeguards.

You will hold end‑to‑end responsibility for the firm’s market data platforms and vendor ecosystem, including Fact Set, LSEG (Refinitiv), Bloomberg, and S&P Capital IQ, enabling GenAI‑powered insights across front office, research, risk, and operational workflows. This role uniquely connects market data ownership, GenAI platform engineering, and enterprise governance, positioning Barclays at the forefront of AI‑enabled capital markets.

To be successful as a Senior GenAI Data VP – Market Data, you should have:

• Experience with design and delivery of GenAI data architectures, including agentic workflows, RAG pipelines, prompt orchestration, and tool calling frameworks
• Ability to manage strategy and integration of market data platforms (Fact Set, LSEG, Bloomberg, S&P Capital IQ), ensuring high quality ingestion, normalization, enrichment, and access for analytics and GenAI use cases
• Experience with designing and overseeing scalable GenAI and machine learning data pipelines, supporting structured and unstructured data across the full AI lifecycle
• Ability to establish and embed data quality, lineage, privacy, and compliance controls to ensure responsible AI use and regulatory alignment
• Stakeholder management skills to support business, product, engineering, and data science teams to identify high impact GenAI use cases and translate them into production grade solutions, defining best practices for evaluation metrics, MCP integration, and scalable data integration across GenAI systems

Other highly valued skills include:

• Experience designing and overseeing GenAI and ML data pipelines, integrating structured and unstructured data for model deployment and evaluation
• Expertise in enterprise GenAI and LLM platforms, including prompt engineering pipelines, vector databases, and retrieval augmented generation (RAG)
• Proven ability to establish data governance, privacy, and compliance frameworks in regulated financial services environments
• Experience partnering with business and engineering teams to deliver production grade GenAI solutions
• Experience setting standards and best practices across data, analytics, and AI teams

This role is located in our Whippany, NJ office.

Minimum Salary: $170,000

Maximum Salary: $230,000

The minimum and maximum salary/rate information above includes only base salary or base hourly rate. It does not include any other type of compensation or benefits that may be available.

Barclays employees are eligible for a suite of competitive and generous employee benefits, including medical, dental and vision coverage, 401(k), life insurance, and other paid leave for qualifying circumstances.

This position is eligible for an incentive award.

To build and maintain the systems that collect, store, process, and analyse data, such as data pipelines, data warehouses and data lakes to ensure that all data is accurate, accessible, and secure.

• Build and maintenance of data architectures pipelines that enable the transfer and processing of durable, complete and consistent data.
• Design and implementation of data warehoused and data lakes that manage the appropriate data volumes and velocity and adhere to the required security measures.
• Development of processing and analysis algorithms fit for the intended data complexity and volumes.
• Collaboration…