Platform Lead (Data)
Location: Global / Hybrid
Department: Digital Transformation
Reports To: Head, Digital Platforms and Products
Experience: 15?25 Years
Role Summary
The Platform Lead (Data) is a strategic leadership role responsible for defining, governing, and implementing the data architecture across all internally developed digital solutions within the Transformation portfolio. This role will be responsible for application data architecture, data modeling standards, enterprise data synchronization, and cross-platform data governance. The position works closely with business leaders, Data Governance teams, DPO, AI teams, Enterprise Architecture, Solution Architecture, Product Teams, and external delivery partners to ensure a scalable, governed, and future-ready data landscape. The role is accountable for ensuring consistent conceptual, logical, and physical data models across all transformation initiatives while driving data quality, interoperability, compliance, and AI readiness.
Key Responsibilities
Enterprise Data Architecture Leadership
- Own and define the data architecture strategy for all Transformation-developed solutions.
- Establish and maintain Conceptual Data Models (CDM), Logical Data Models (LDM), and Physical Data Models (PDM) for all developed products and solutions.
- Define data architecture principles, standards, naming conventions, best practices across platforms.
- Review and approve data architecture designs for all new applications and major enhancements.
- Ensure solutions align with organizational data governance, security, privacy, and compliance requirements.
Data Governance & Business Alignment
- Act as the primary interface between the Platform teams and all other teams, including Data Governance Teams, Data Protection Office, Enterprise Architecture, Business Stakeholders, Product Owners, AI Teams, Data Engineering Teams, Analytics teams, Vendor Delivery Teams
- Participate in governance forums and architecture review boards.
- Ensure alignment between business processes, data domains, enterprise information architecture.
- Drive data lineage, metadata management, master data, and reference data strategies.
Data Modeling Leadership
- Lead and mentor data modelers across all internal and vendor delivery teams.
- Work hands-on, wherever necessary, on creating models especially on complex projects
- Establish consistent modeling standards and quality controls.
- Conduct architecture and data model reviews.
- Guide teams in developing Entity Relationship Diagrams (ERD), Logical Data Models, Physical Data Models, Data Dictionaries, Mapping Specifications
- Ensure data models support operational applications, analytics, reporting, and AI use cases.
Data Synchronization & Integration Accountability
- Own enterprise-wide data synchronization strategy across Transformation platforms.
- Design and govern data integration patterns between internal and external systems.
- Ensure consistent master data, reference data, and shared business entities across applications.
- Define architecture for APIs, event-driven integrations, data pipelines, and platform interoperability.
- Eliminate duplicate data domains and conflicting data ownership models.
AI & Analytics Enablement
- Partner with AI teams to ensure data structures support advanced analytics and AI initiatives.
- Define data models optimized for: AI Agents, ML, Predictive Analytics, Knowledge Graphs, Data Products
- Promote enterprise-wide data discoverability and reusability.
Stakeholder & Vendor Management
- Provide technical leadership to multiple project teams simultaneously.
- Review and govern deliverables from vendor organizations.
- Participate in project planning, estimation, solution reviews, and architecture governance meetings.
- Influence senior leadership on data strategy and architecture decisions.
Must Have Skills
- Data Modeling & Database Design
- Strong expertise in conceptual, logical, and physical data modeling.
- Extensive knowledge of normalization and dimensional data modeling.
- Strong experience defining data associations and business relationships efficiently.
- Expertise in enterprise-scale database design and optimization.
- Database Technologies
- Hands-on experience with: SQL Server, AWS, Azure SQL, Relational Database Management Systems (RDBMS), Data Warehousing platforms
- SQL & Data Processing
- Strong practical experience in: SQL scripting, Database query optimization, Complex SQL development, Stored procedures, Views, Functions, Triggers, Data migration scripts, Data transformation logic
- Governance & Architecture
- Data Governance frameworks | Enterprise Information Architecture
- Data Quality Management | Data Lineage and Traceability
- Metadata Management | Master Data Management (MDM)
- Data Privacy and Compliance
Mandatory Responsibilities
- Participate in requirements definition, analysis, design of logical and physical data models for databases.
- Conduct data model reviews with project teams and architects.
- Ensure database designs efficiently support BI, reporting, AI, and operational requirements.
- Manage data model documentation, version control, and model governance.
- Present and review data models with both technical and business audiences.
- Develop and maintain Entity Relationship Diagrams (ERD).
- Define associations, cardinality, and enterprise business relationships efficiently.
- Work extensively with Snowflake, SQL Server, and other enterprise RDBMS platforms.
- Develop SQL scripts and queries for scalable and efficient data processing.
- Design, review, and optimize stored procedures and database objects.
Preferred Qualifications
- TOGAF certification / Certified Data Management Professional (CDMP).
- Azure Data Engineer or Azure Solutions Architect Certification.
- Experience with enterprise platforms and digital transformation programs.
- Exposure to AI/ML data ecosystems and modern data platforms.
Success Measures
The Role Will Be Measured On
- Consistency of data architecture across solutions / Reduction in duplicate and conflicting enterprise data.
- Quality and reusability of data models / AI readiness of data structures.
- Compliance with Data Governance standards.
- Successful synchronization of master and transactional data across platforms/solutions.
- Effective leadership of internal and vendor data modeling teams.
- Adoption of enterprise data architecture standards across Transformation initiatives.
Data Modelling
