Senior Data Modeller

EXL (NASDAQ: EXLS) is a global data and artificial intelligence ("AI") company that offers services and solutions to reinvent client business models, drive better outcomes and unlock growth with speed. EXL harnesses the power of data, AI, and deep industry knowledge to transform businesses, including the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 60,000 employees spanning six continents. For more information, visit www.exlservice.com . Role: Lead Data Modeller BU/Segment: Banking / Analytics Location: London, United Kingdom (Hybrid working with 3 days a week in the office and travel to Halifax twice a month) Employment Type: Umbrella Contract (Inside IR35) to start ASAP. We are also open to a Permanent position. We are seeking an experienced Data Modelling Architect to design and govern enterprise-wide data models that support business intelligence, analytics, data warehousing, AI/ML, and operational applications. The ideal candidate will have strong expertise in conceptual, logical, and physical data modelling, data architecture, cloud data platforms, and data governance. The role requires close collaboration with business stakeholders, data engineers, solution architects, analysts, and application teams to ensure scalable, high-quality, and reusable data designs across the organization. As part of your duties, you will be responsible for: Data Architecture & Modelling Design and maintain Enterprise Data Models (EDM) aligned with business objectives. Create Conceptual, Logical, and Physical Data Models for enterprise applications and analytics platforms. Define data standards, naming conventions, and modelling best practices. Develop dimensional models (Star Schema, Snowflake Schema) for reporting and analytics. Design operational and transactional database models supporting business applications. Establish master data, reference data, and canonical data models. Data Governance & Quality Ensure adherence to data governance policies and architectural standards. Define metadata management, lineage, and data catalog requirements. Collaborate with governance teams to implement data quality frameworks. Assess and resolve data integrity, consistency, and duplication issues. Cloud Data Platforms Architect data models for platforms such as: Google BigQuery Azure Synapse Analytics Azure Data Lake Snowflake Databricks AWS Redshift Support modern Lakehouse and Data Warehouse architectures. Stakeholder Collaboration Translate business requirements into scalable data solutions. Partner with Data Engineers and BI teams during solution design and implementation. Conduct data architecture reviews and provide technical leadership. Mentor data modelers and engineering teams. Performance & Optimization Optimize database structures for scalability and performance. Design partitioning, indexing, and data retention strategies. Recommend improvements to data architecture and data flow processes. Qualifications and experience we consider to be essential for the role: Data Modelling ER Modelling Dimensional Modelling Data Vault 2.0 Normalization & Denormalization Master Data Management (MDM) Database Technologies SQL Server BigQuery SAS ProcSQL Oracle PostgreSQL Snowflake Azure SQL Database Tools ERwin Data Modeler ER/Studio PowerDesigner Visio SQL Developer Governance & Metadata Data Lineage Data Catalog Data Quality Frameworks Metadata Management Bachelor's degree in Computer Science, Information Systems, Engineering, or related field. Master's degree preferred. Data Architecture or Cloud certifications are highly desirable. Skills and Personal attributes we would like to have: Preferred Certifications Microsoft Certified: GCP Microsoft Azure Solutions Architect Expert Snowflake SnowPro Certification Databricks Data Engineer Professional Key Competencies Enterprise Data Architecture Stakeholder Management Strategic Thinking Problem Solving Data Governance Technical Leadership Communication & Presentation Skills Success Metrics (KPIs) Reduction in data quality issues. Compliance with enterprise data standards. Reusability of enterprise data models. Improvement in reporting and analytics performance. Successful implementation of enterprise data architecture initiatives. User adoption of governed data assets.