Website:
voltuswave.ai
Job details:
Job Description – Enterprise Data Architecture & Platform Lead
Engagement Type: Full-Time On-Site Professional Resource Duration: 12 Months Seniority: Senior SME / Lead Industry: Banking
1. 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 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.
2. Key Responsibilities
Enterprise Data Architecture
• 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.
Data Discovery, Profiling & Mapping
• 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.
Banking Data Modelling
• 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.
Official bank muscat Version 8.0
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Business Data Enablement
• Work with business functions to understand their data needs, existing challenges and opportunities for improved data utilization.
• Translate business needs into appropriate enterprise data capabilities rather than treating individual requests as standalone technical requirements.
• Facilitate agreement on common data definitions, business rules and ownership across relevant stakeholders.
• Prioritize requirements based on business value, strategic importance, regulatory need, reuse potential, complexity and implementation effort.
• Constructively challenge low-value, duplicative or unnecessarily complex requirements and recommend more effective alternatives.
• Guide the rollout and adoption of improved data capabilities, including self-service data and analytics.
• Identify additional capabilities required to improve data accessibility, usability, quality and timeliness.
Data Integration & Engineering Architecture
• Define appropriate integration and ingestion approaches, including batch, ETL/ELT, CDC, APIs, streaming and event-driven patterns.
• Select appropriate patterns based on data characteristics, source-system capabilities, timeliness requirements and business needs.
• Guide approaches for structured, semi-structured and unstructured data.
• Define appropriate data transformation, standardization, enrichment and serving patterns.
• Ensure data pipelines incorporate suitable automation, monitoring, reconciliation, traceability, reliability and maintainability.
Enterprise Data Platform
• Define the functional and technical capabilities required from an enterprise data platform.
• Assess technology options based on organizational requirements and architectural fit.
• Evaluate capabilities across ingestion, storage, processing, transformation, orchestration, metadata, lineage, governance, quality, security and consumption.
• Assess technical trade-offs and provide clear recommendations, avoiding unnecessary complexity, cost or technology dependency.
• Ensure platform capabilities support reporting, analytics, self-service, advanced analytics and future AI/data-science requirements where applicable.
• Provide technical assurance over platform architecture and implementation designs.
• Provide data platform operating model and governance mechanism.
Data Governance, Quality & Metadata
• Ensure data governance is appropriately embedded within the data architecture and its implementation.
• Define practical approaches for metadata management, data cataloguing and lineage.
• Establish data-quality principles, controls and measurement approaches.
• Support definition of appropriate data ownership and stewardship.
• Ensure security, privacy, access control, retention and regulatory requirements are incorporated into data solutions.
Technical Delivery Leadership
• Convert strategic and architectural objectives into practical, executable initiatives.
• Provide technical direction to data architects, engineers, analysts and other supporting resources.
• Establish technical standards and acceptance criteria and assess key deliverables against them.
• Identify technical and architectural risks early and drive their resolution.
• Provide technical oversight across concurrent data activities and dependencies.
• Work alongside project/program management while owning the technical direction and outcomes.
• Independently assess and challenge solutions proposed by internal teams and external parties where required.
• Ensure effective knowledge transfer and development of sustainable internal data capabilities.
3. Mandatory Banking Domain Knowledge Official bank muscat Version 8.0
This Document is classified as Official BM/CPD/RFP Confidential Page 10 of 12
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.
4. Required Experience
The candidate must demonstrate:
• 12+ years of relevant professional experience in enterprise data architecture, data engineering, data platforms or closely related disciplines.
• Minimum 5 years of direct banking or financial-services data experie
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