Role Overview / Purpose
We are seeking an experienced Data Modeller Lead to help shape our next-generation data architecture and enterprise data models as part of the global Data & Analytics (D&A) team. This senior role, ideal for professionals with 8–12 years of experience, is responsible for designing and implementing a unified, enterprise-wide data model that aligns with standardized business processes and major modernization initiatives, including migration from legacy ERP systems to modern, cloud-based data platforms.
As the Data Modeller Lead, you will act as a functional data architecture expert at the intersection of business process design and technical data modeling. You will bridge the current data landscape with a future-state unified data model, ensuring that critical business requirements and key performance indicators (KPIs) are translated into robust, scalable data structures that work seamlessly across legacy and new systems.
A core aspect of this role is end-to-end accountability for Master Data Management (MDM) and hierarchy design within the enterprise data lake or lakehouse environment. You will own the design, implementation, and governance of master data domains and hierarchies to ensure consistency, quality, and alignment with analytical, reporting, and downstream consumption needs.
Key Responsibilities
- Enterprise Data Model Design: Lead the development of conceptual, logical, and physical data models for the enterprise analytics environment. Design data structures for data lakes, warehouses, and data marts that support business-critical domains such as Finance, Supply Chain, and Procurement. Ensure adherence to best practices and support for both relational and non-relational data paradigms.
- Current-to-Future State Mapping: Own the mapping of legacy data objects and schemas to the new harmonized data model. Identify and resolve gaps or inconsistencies between current-state and target-state designs, ensuring continuity of key business metrics and KPIs.
- Data Specifications & Documentation: Translate business requirements into clear and detailed data specifications. Produce and maintain high-quality data modeling artifacts, including entity-relationship diagrams, data dictionaries, and mapping documents, to guide engineering teams and support long-term maintainability.
- Cross-Functional Collaboration: Work closely with business process owners and functional teams to co-create and validate data models that support standardized processes and analytics needs. Act as a bridge between business and technical teams, facilitating shared understanding of data structures and definitions.
- Data Model Implementation: Partner with data engineering and platform teams to implement data models on modern cloud-based analytics platforms. Guide the build-out of scalable data pipelines from source systems into the enterprise data lake or warehouse, ensuring alignment with modeling standards.
- Testing & Data Validation: Define and execute robust testing strategies to validate data migrations and transformations. Support system integration testing and user acceptance testing by resolving data-related issues and ensuring accuracy and consistency of analytical outputs.
- Data Architecture & Performance Optimization: Establish and enforce enterprise data modeling standards, including naming conventions and normalization guidelines. Review and optimize designs for performance, scalability, and sustainability at enterprise scale.
- Business Enablement & Adoption: Enable analytics, reporting, and data science teams to effectively use the new data model. Provide clear documentation, training materials, and guidance to drive adoption and maximize business value from the unified data foundation.
- Data Governance & Quality: Champion strong data governance and data quality practices. Define data ownership and stewardship, maintain metadata and data dictionaries, and ensure compliance with security, privacy, and regulatory requirements. Implement controls to ensure consistent and high-quality master and reference data across systems.
- Leadership & Best Practices: Provide technical leadership and mentorship to data modelers and related roles. Promote reusable design patterns, continuous improvement, and alignment with the broader Data & Analytics strategy.
Master Data & Hierarchy Responsibilities
- Master Data Domain Ownership: Own the design and standardization of enterprise master data domains such as Product, Customer, Vendor, Finance, and Supply Chain within the analytics data platform. Establish a harmonized, enterprise-wide approach that enables a single source of truth.
- Hierarchy Design & Management: Design, implement, and maintain global hierarchies (e.g., product, customer, financial, organizational) within the enterprise data lake or lakehouse. Ensure hierarchies support consistent roll-ups, drill-downs, and cross-functional reporting across markets and regions.
- Alignment with Analytics & Reporting: Ensure master data and hierarchies are designed to directly support analytical, reporting, and KPI requirements. Collaborate with data consumers to ensure master data attributes and hierarchy levels enable effective insights and decision-making.
- MDM Integration: Work closely with Master Data Management platforms and teams to ensure governed ingestion of master data and hierarchy changes into the analytics environment. Ensure master data updates are controlled, traceable, and consistently applied across downstream systems.
- Master Data Quality & Metadata: Define and monitor data quality rules for master data and hierarchies. Ensure comprehensive metadata, lineage, and business definitions are maintained so master data is intuitive, trustworthy, and easy to consume.
Required Skills & Experience
- Bachelor's or Master's degree in Computer Science, Information Systems, Data Analytics, or a related field.
- Approximately 8–12 years of experience in data modeling, data architecture, or data engineering within large, complex enterprise environments.
- Deep expertise in conceptual, logical, and physical data modeling, including normalization and dimensional modeling techniques.
- Strong understanding of enterprise systems and business data, particularly ERP-driven domains.
- Hands-on experience with data integration and transformation (ETL/ELT) in modern data platforms.
- Proficiency with cloud-based analytics environments and big data architectures.
- Solid knowledge of data governance, data quality, metadata management, and MDM concepts.
- Excellent analytical, problem-solving, and communication skills.
- Proven ability to work effectively in cross-functional, global, and matrixed environments.
Preferred / Nice-to-Have Skills
- Experience in large, data-intensive global enterprises or FMCG environments.
- Exposure to next-generation ERP platforms and enterprise MDM solutions.
- Familiarity with advanced analytics, data science, or machine learning use cases.
- Relevant professional certifications in data architecture, cloud platforms, or data management.
Stakeholder Interaction & Ways of Working
- Collaborate closely with business process owners, functional teams, and global stakeholders to ensure data models align with business needs.
- Partner with data engineering, analytics, and platform teams to translate functional designs into scalable technical solutions.
- Work with project, testing, and governance teams to ensure data quality, compliance, and successful delivery.
- Engage analytics, BI, and data science teams to drive adoption and continuous improvement of the data model.
- Coordinate with external partners and vendors where required, ensuring alignment with enterprise standards and long-term strategy.
Leadership & Behavioral Expectations
- Demonstrates inclusive leadership and a collaborative mindset.
- Focuses on high-impact outcomes that matter most to the business.
- Thinks strategically and proactively anticipates future data and business needs.
- Delivers with excellence, attention to detail, and strong ownership of outcomes.
- Communicates clearly and confidently with both technical and non-technical stakeholders.
- Operates effectively in fast-paced, complex environments and drives continuous improvement.
The Data Modeller Lead plays a pivotal role in establishing a high-quality, well-governed data foundation that enables enterprise-wide analytics, reporting, and data-driven decision-making at scale.
