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
