Senior Data Principal

Kaplan International | AI & Data Team

Reporting to: VP, AI & Data

About the role

We are looking for an experienced data leader to help shape and drive our data strategy across Kaplan business units. This role will lead data strategy and delivery across key business units and Shared Services, while working across Kaplan to strengthen common data standards, governance, definitions and interoperability.

Our businesses already have established data teams, platforms and capabilities. This role will build on those investments rather than replace existing teams or mandate a single technology stack. The focus is on identifying where common approaches make sense and making better use of our collective data.

As we become more AI-first, we need to look beyond traditional data management and reporting. A key priority will be making our data more usable for AI, including exploring common semantic models, knowledge layers and context engineering. There is also an opportunity to use our data, knowledge and expertise to build new AI-enabled products, strengthen existing propositions and create new commercial opportunities.

Key responsibilities

1. Data strategy and delivery

- Develop and drive the data strategy for the UK businesses in scope and all Shared Services, aligned with business priorities and our wider AI ambitions.
- Build a clear understanding of the data landscape, including key data domains, platforms, capabilities, ownership and gaps.
- Work with business and technology teams to prioritise and drive delivery of data initiatives that support operational improvements, growth and AI adoption.
- Develop a practical roadmap for data foundations, integration, data products and AI readiness.
- Identify opportunities to make better use of existing platforms, including Databricks, Microsoft Fabric and other technologies already in place.
- Work with AI and engineering teams to ensure priority initiatives have the data foundations and business context they need.
- Assess priorities based on business value, technical feasibility, investment required and potential for reuse.

2. KI-wide data standards and governance

- Work across all KI businesses to establish common data standards, definitions and governance principles.
- Develop consistent business definitions and data standards for priority domains where a shared approach is needed.
- Work with BU data leaders on common approaches to data ownership, quality, metadata, lineage, data contracts and access.




- Support a federated approach to data governance, recognising that businesses retain accountability for their platforms and local delivery.
- Identify where common standards should apply and where business-specific requirements justify different approaches.
- Work through existing governance forums to agree standards, support adoption and manage exceptions.
- Align with the Kaplan Intelligence Hub and wider Kaplan data initiatives to avoid duplication.
- Work closely with architecture, security, privacy and legal teams to ensure data standards and practices meet relevant requirements.

3. Semantic layer, interoperability and AI-ready data

- Shape our approach to common semantic models and context layers, making it easier for AI applications to access trusted data and business knowledge.
- Work with data teams to establish consistent definitions and relationships between key business entities, such as students, leads, programmes, enrolments, conversion and progression.
- Explore how semantic modelling, ontologies, knowledge graphs and identity resolution could help connect information across different businesses and systems.
- Identify practical approaches to interoperability without requiring a single technology stack.
- Work with engineering teams to develop reusable data products and interfaces that can support different business needs.
- Explore how structured data, business rules, metadata and organisational knowledge can provide reliable context for AI applications and agents.
- Ensure proposed approaches align with common standards and existing governance, privacy and security requirements.

4. Data products and commercial opportunities

- Identify opportunities to use our collective data, knowledge and expertise to create business value and competitive advantage.
- Explore where shared intelligence and data capabilities could enable new AI-native products and services.
- Work with business and commercial teams on opportunities such as student and programme matching, learner progression, personalised learning, partner intelligence and demand forecasting.




- Assess opportunities based on customer need, differentiation, technical feasibility, commercial potential and investment required.
- Support business cases, prototypes and pilots alongside product, commercial and engineering teams.
- Identify opportunities to reuse data and intelligence capabilities across different products and businesses.
- Explore potential external commercialisation of data-enabled products and services, with appropriate consideration of data rights, privacy and security.

About you

We are looking for a senior data skilled with solid technical expertise, strategic thinking and commercial awareness.

You should understand up-to-date data architecture, governance and what it takes to make data usable for AI. You should also be able to look beyond the technology and identify where better use of data could create meaningful business value.

This role requires someone who can lead delivery, influence senior stakeholders and work effectively across established data and technology teams.

Essential experience

- Significant experience in senior data leadership, data strategy or enterprise data architecture.
- Experience developing and delivering data strategies across complex organisations.
- Strong understanding of modern data platforms, data modelling, semantic layers, data products and integration approaches.
- Experience establishing data governance frameworks, common data standards, ownership models and data quality practices.
- Experience working in federated or decentralised data environments.
- Understanding of how data architecture, metadata and business context support AI applications.
- Ability to translate data strategy into practical initiatives and work with technical teams to drive delivery.
- Strong commercial awareness and experience linking data investment to business outcomes.
- Ability to influence senior business and technology stakeholders.
- Practical understanding of data privacy, security and responsible data use.

Desirable experience

- Experience with Databricks, Microsoft Fabric or similar enterprise data platforms.
- Knowledge of semantic modelling, ontologies, knowledge graphs, identity resolution and context engineering for AI.
- Experience developing data-driven or AI-enabled products, including commercially focused products or services.
- Experience in education, learning technology or other service-led businesses.

📌 Senior Data Principal (Bengaluru)
🏢 Kaplan
📍 Bengaluru