Enterprise Data Architecture & Platform Lead

Enterprise Data Architecture & Platform Lead Engagement Type: Full-Time, On-Site Professional Resource Duration: 12 Months Seniority: Senior SME / Lead Industry: Banking Role Purpose We are seeking a highly experienced Enterprise Data Architecture & Platform Lead to provide technical and strategic leadership for an enterprise-wide data transformation initiative. The role requires a senior practitioner with deep enterprise data expertise, strong banking-domain knowledge, and practical implementation experience. The individual will work across Business, Technology, Architecture, Data, and Analytics functions to shape and drive enterprise data capabilities. This is not a Project Manager or purely conceptual Enterprise Architect role. The position requires hands-on leadership from strategy and architecture through practical implementation and adoption. Key Responsibilities Enterprise Data Architecture • Define and evolve enterprise data architecture. • Assess existing data landscapes, integrations, dependencies, and limitations. • Define architecture principles for data storage, processing, integration, transformation, and consumption. • Evaluate data warehouse, data lake, lakehouse, medallion, and domain-oriented architectures. • Translate architecture principles into practical technical standards and designs. • Ensure scalability, performance, security, resilience, maintainability, regulatory compliance, and cost effectiveness. Data Discovery, Profiling & Mapping • Lead data discovery, profiling, classification, and analysis. • Guide development of data dictionaries, metadata, lineage, and source-to-target mappings. • Identify data-quality issues, duplication, inconsistencies, gaps, and redundant data. • Ensure appropriate understanding of business meaning, ownership, relationships, and usage of critical data. Banking Data Modelling • Lead enterprise and domain-level banking data modelling. • Translate banking processes, products, entities, and relationships into appropriate data structures. • Establish common definitions and reusable data entities. • Rationalize customer, account, transaction, and product representations across source systems. • Develop reusable models supporting multiple business use cases. Data Integration & Engineering Architecture • Define integration and ingestion approaches covering batch, ETL/ELT, CDC, APIs, streaming, and event-driven patterns. • Guide structured, semi-structured, and unstructured data approaches. • Define transformation, standardization, enrichment, and serving patterns. • Ensure automation, monitoring, reconciliation, traceability, reliability, and maintainability of data pipelines. Enterprise Data Platform • Define functional and technical capabilities for an enterprise data platform. • Evaluate technology options based on architectural fit and organizational requirements. • Assess ingestion, storage, processing, orchestration, metadata, lineage, governance, quality, security, and consumption capabilities. • Support reporting, analytics, self-service, advanced analytics, and future AI/data-science requirements. • Define data-platform operating models and governance mechanisms. Data Governance, Quality & Metadata • Embed data governance into the enterprise data architecture. • Define metadata management, data cataloguing, and lineage approaches. • Establish data-quality principles, controls, and measurement. • Support data ownership and stewardship. • Incorporate security, privacy, access control, retention, and regulatory requirements. Technical Delivery Leadership • Convert strategic architecture into executable initiatives. • Provide technical leadership to architects, engineers, and analysts. • Establish technical standards and acceptance criteria. • Identify and resolve architectural and technical risks. • Provide oversight across concurrent data initiatives. • Ensure knowledge transfer and sustainable internal data capabilities. Mandatory Banking Domain Knowledge Candidates must have strong knowledge across banking data domains including: • Customer & Party • Accounts & Deposits • Loans & Lending • Cards • Payments & Transfers • Transactions • Products & Pricing • Digital & Physical Channels • General Ledger & Financial Data • Risk & Compliance • KYC / AML • Master & Reference Data Required Experience • 12+ years of relevant experience in enterprise data architecture, data engineering, data platforms, or related disciplines. • Minimum 5 years of direct banking/financial-services data experience. • Experience as an Enterprise Data Architect, Senior Data Architect, Data Platform Lead, Data Transformation Lead, or equivalent. • Proven experience leading significant enterprise data modernization/transformation initiatives from architecture through implementation. • Strong experience across multiple banking data domains and complex legacy environments. • Practical experience with enterprise data modelling, data engineering, and modern data platforms. • Experience engaging senior business and technology stakeholders within regulated environments. Technical Knowledge Strong knowledge of: Enterprise Data Architecture | Banking Data Architecture | Data Modelling | Data Warehousing | Dimensional Modelling | ODS | Data Lake | Lakehouse | Medallion Architecture | ETL/ELT | CDC | APIs | Streaming | Event-Driven Architecture | Metadata Management | Data Cataloguing | Data Lineage | Data Observability | Data Quality | Master Data | Reference Data | Data Security | BI & Analytics | AI/Data Science Data Requirements | Cloud | On-Premises | Hybrid Architecture Show more Show less

12+ years of relevant experience in enterprise data architecture, data engineering, data platforms, or related disciplines. Minimum 5 years of direct banking/financial-services data experience. Experience as an Enterprise Data Architect, Senior Data Architect, Data Platform Lead, Data Transformation Lead, or equivalent. Proven experience leading significant enterprise data modernization/transformation initiatives from architecture through implementation. Strong experience across multiple banking data domains and complex legacy environments. Practical experience with enterprise data modelling, data engineering, and modern data platforms. Experience engaging senior business and technology stakeholders within regulated environments.

Define and evolve enterprise data architecture; Assess existing data landscapes, integrations, dependencies, and limitations; Define architecture principles for data storage, processing, integration, transformation, and consumption; Evaluate data warehouse, data lake, lakehouse, medallion, and domain-oriented architectures; Translate architecture principles into practical technical standards and designs; Ensure scalability, performance, security, resilience, maintainability, regulatory compliance, and cost effectiveness; Lead data discovery, profiling, classification, and analysis; Guide development of data dictionaries, metadata, lineage, and source-to-target mappings; Identify data-quality issues, duplication, inconsistencies, gaps, and redundant data; Ensure appropriate understanding of business meaning, ownership, relationships, and usage of critical data; Lead enterprise and domain-level banking data modelling; Translate banking processes, products, entities, and relationships into appropriate data structures; Establish common definitions and reusable data entities; Rationalize customer, account, transaction, and product representations across source systems; Develop reusable models supporting multiple business use cases; Define integration and ingestion approaches covering batch, ETL/ELT, CDC, APIs, streaming, and event-driven patterns; Guide structured, semi-structured, and unstructured data approaches; Define transformation, standardization, enrichment, and serving patterns; Ensure automation, monitoring, reconciliation, traceability, reliability, and maintainability of data pipelines; Define functional and technical capabilities for an enterprise data platform; Evaluate technology options based on architectural fit and organizational requirements; Assess ingestion, storage, processing, orchestration, metadata, lineage, governance, quality, security, and consumption capabilities; Support reporting, analytics, self-service, advanced analytics, and future AI/data-science requirements; Define data-platform operating models and governance mechanisms; Embed data governance into the enterprise data architecture; Define metadata management, data cataloguing, and lineage approaches; Establish data-quality principles, controls, and measurement; Support data ownership and stewardship; Incorporate security, privacy, access control, retention, and regulatory requirements; Convert strategic architecture into executable initiatives; Provide technical leadership to architects, engineers, and analysts; Establish technical standards and acceptance criteria; Identify and resolve architectural and technical risks; Provide oversight across concurrent data initiatives; Ensure knowledge transfer and sustainable internal data capabilities;