Enterprise Data Architecture & Platform Lead

Job Description – Enterprise Data Architecture & Platform Lead

Engagement Type: Full-Time On-Site Professional Resource

Duration: 12 Months

Seniority: Senior SME / Lead

Industry: Banking

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 who combines 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 the evolution of the organization’s enterprise data capabilities. The successful candidate will be expected to translate business needs into scalable data capabilities, guide architecture and technical decisions, and ensure that the overall approach remains practical, sustainable and focused on business value. This is not a Project Manager role or a purely conceptual Enterprise Architect position
. The role requires someone capable of leading the subject matter from strategy and architecture through to practical implementation and adoption.

• Lead the definition and evolution of the organization’s enterprise data architecture.
• Assess the existing data landscape, including data structures, integrations, dependencies and limitations, and determine how these should evolve toward the target state.
• Define architecture principles and patterns covering data storage, processing, integration, transformation and consumption.
• Evaluate architectural approaches including data warehouse, data lake, lakehouse, medallion and domain-oriented patterns and determine where they are appropriate.
• Translate architecture principles into practical technical standards and implementable designs.
• Ensure architecture decisions appropriately balance scalability, performance, security, resilience, maintainability, regulatory requirements, cost and operational complexity.
• Review key technical designs and ensure alignment with the overall enterprise data direction.

• Provide technical direction for systematic discovery and understanding of data across existing environments and source systems.
• Define approaches and standards for data extraction, profiling, classification and analysis.
• Guide the development of data dictionaries, metadata, lineage and source-to-target mappings.
• Drive identification of data-quality issues, gaps, duplication, inconsistencies and redundant data.
• Ensure technical analysis captures the business meaning, ownership, relationships and usage of critical data elements.
• Review the quality and completeness of outputs produced by supporting resources and identify areas requiring further investigation.

• Lead the development of enterprise and domain-level banking data models.
• Work with business stakeholders to understand banking processes, products, entities, relationships and information requirements and translate them into appropriate data structures.
• Define mappings between source-system data, enterprise data models and consumption requirements.
• Establish common definitions and reusable data entities across banking functions and systems where appropriate.
• Rationalize differences in how common entities such as customers, accounts, transactions and products are represented across source systems.
• Ensure data models are designed for reuse across multiple business use cases rather than around individual reports or requirements.

Strong banking-domain knowledge is mandatory for this role.

The candidate must understand the structures, terminology and relationships across major banking data domains, including:
Customer and party

• Accounts and deposits
• Loans and lending
• Cards
• Payments and transfers
• Transactions
• Products and pricing
• Digital and physical channels
• General ledger and financial data
• Risk and compliance
• KYC / AML
• Master and reference data

The candidate should understand how these domains interact and how common business entities may be represented differently across core banking, lending, cards, payments, CRM, digital channels and other banking systems.

The level of banking knowledge should enable the individual to engage directly with banking stakeholders, understand the context behind data requirements and make informed modelling and architecture decisions without requiring extensive banking-domain orientation.