Data Modeling Lead, Vice President

About this role

Vice President – Data Modelling

Position Overview:

BlackRock is seeking a highly experienced and strategic Vice President to lead its growing and dynamic Data Modelling function. This senior role is responsible for driving the design, creation, and maintenance of data models that power critical analytics, regulatory, and operational objectives across the firm. The VP will collaborate with business stakeholders, technical teams, and external vendors to ensure that data models are robust, scalable, and aligned with enterprise data governance standards. The ideal candidate will bring at least 7 years of experience in data modelling and data management, with a proven track record of delivering large-scale, data-driven initiatives. Experience in modelling for financial securities reference data is a plus, but strong data modelling expertise in any domain is sufficient.

Key Responsibilities

Leadership & Strategic Direction

• Strategic Vision: Define and execute the strategic vision for data modelling across the organization, setting the direction for how data models are designed, maintained, and optimized.

• Business Alignment: Work closely with senior leadership and business stakeholders to ensure data models address current and future business needs while adhering to industry best practices.

• Thought Leadership: Advocate for the strategic importance of data modelling within BlackRock, championing its value in driving data-driven decision-making.

Data Governance & Quality Oversight

• Governance Alignment: Ensure that all data models comply with BlackRock’s data governance framework, emphasizing data accuracy, completeness, and consistency.

• Quality Standards: Establish and enforce robust data quality standards and governance policies that integrate seamlessly with data modelling processes.

• Continuous Improvement: Oversee data health assessments related to model performance and reliability, ensuring ongoing enhancements to meet regulatory and operational needs.

Data Product Lifecycle Management

• Model Development: Oversee the end-to-end lifecycle of data models—conceptual, logical, and physical—ensuring they are fit for purpose and strategically aligned with organizational goals.

• Collaboration: Drive effective coordination among product management, engineering, and business teams to integrate new or updated data models into data products.

• Scalability & Sustainability: Balance short-term business demands with long-term architectural objectives, designing models that are scalable and adaptable to future needs.

Requirements Gathering & Documentation

• Stakeholder Engagement: Lead efforts to gather and document comprehensive business requirements for data modelling initiatives, collaborating closely with stakeholders to translate business needs into model specifications.

• Technical Translation: Oversee the documentation process to ensure clarity and consistency, effectively bridging the gap between technical requirements and business objectives.

• Actionable Tasks: Break down complex modelling challenges into actionable tasks to facilitate efficient and accurate model development.

Vendor & Stakeholder Management

• External Partnerships: Manage relationships with data and software vendors, ensuring that third-party solutions meet BlackRock’s data model requirements and quality standards.

• Cross-Functional Collaboration: Work in tandem with internal teams across business, technology, and operations to ensure that delivered models meet stakeholder needs on time and within budget.

• Negotiation & Influencing: Serve as a key liaison with both internal and external partners, negotiating priorities, timelines, and resources to achieve optimal modelling outcomes.

Collaboration & Communication

• Cross-Team Leadership: Lead and foster collaboration among technical and non-technical teams, ensuring that complex modelling concepts are clearly understood and appropriately applied.

• Influence & Alignment: Communicate the data modelling strategy to senior leadership, aligning cross-functional efforts with organizational priorities.

• Knowledge Sharing: Promote a culture of data literacy and best practices in data modelling through regular training and guidance.

Data Model Support & Problem Resolution

• Issue Resolut