Data Architect MMH

Role Purpose

The Data Architect is accountable for designing, implementing, and evolving Momentum Corporate’s modern data architecture across on-prem and cloud environments, enabling secure, scalable, compliant and cost-effective data capabilities that support business strategy, advanced analytics, and regulatory requirements.

This role establishes the target state data platform architecture (cloud-native and hybrid), ensures data assets are structured, governed, and optimized, and provides architecture leadership across domains to unlock value across the value chain (customer service, risk and compliance, digital transformation).

In addition, the role explicitly ensures the data platform is AI/ML-ready, by enabling capabilities such as high-quality curated datasets, feature and inference data patterns, metadata/lineage, and operational controls that support reliable AI/ML development and deployment at scale.

• BSc in Computer Science, Information Systems, or related field.
• 8+ years’ experience as a Data Architect
• 3+ years designing and implementing cloud-based data platforms
• Financial services industry knowledge
• Experience with Agile development methodologies
• AWS Certified Data Analytics / Solutions Architect (advantageous)
• TOGAF, DAMA, or CDMP certification (advantageous)

• Strong understanding of data modelling
• Experience with data architecture in cloud platforms (AWS and Azure)
• Strong experience with data lake, lakehouse, and data warehouse architecture.
• Familiarity with financial data domains (customer, product, transaction, risk, and regulatory data).
• Ability to communicate complex data concepts to non-technical stakeholders
• Awareness of current and emerging technologies (e.g., cloud computing, artificial intelligence, IoT)
• Understanding of digital transformation initiatives and how to integrate new technologies into existing business processes
• Cybersecurity principles, practices, and regulations to protect data and systems
• Financial services industry knowledge
• Agile development methodologies

• Translate business needs into data-driven architectural solutions and roadmaps aligned to enterprise strategy.
• Provide architectural leadership and governance for data initiatives across business domains.
• Develop and maintain enterprise data models (conceptual, logical, physical) and ensure consistent modelling standards across domains.
• Define and maintain reference architectures, target state blueprints, and reusable patterns for ingestion, storage, transformation, serving, and analytics.
• Align with other IT and data architectural areas within the group to ensure coherence and interoperability.

• Define architecture for data ingestion, processing, storage, and analytics using modern tooling and practices.
• Design cloud-native and hybrid data platforms (AWS primary; Azure familiarity beneficial).
• Design and oversee implementation of cloud-based data platforms such as data lakehouse, warehouse, or data mesh-aligned architectures.
• Configure and manage the operational cloud data platform, including key architectural decisions around scalability, availability, resilience, and cost controls.
• Ensure an Infrastructure as Code (IaC) approach for platform provisioning and repeatable environment builds.

• Define and document data integration frameworks and engineering standards (pipeline patterns, naming standards, schema evolution patterns, testing requirements).
• Collaborate with data engineers to ensure implementation aligns to architectural standards and nonfunctional requirements.
• Standardise reusable data pipelines and reusable data products that support multiple consumers reliably.
• Oversee implementation of data pipelines and APIs for real-time and batch data movement.
• Integrate structured and unstructured data from multiple internal and external systems with appropriate quality, security, and governance controls.
• Provide architecture oversight throughout delivery lifecycle (design reviews, guardrails, implementation guidance, operational readiness).

• Partner with governance stakeholders to define and implement data standards, metadata management, lineage, and cataloguing practices.
• Ensure data quality is measurable and managed through practical controls (quality rules, thresholds, monitoring, incident handling, and remediation workflows).
• Define data lifecycle and retention patterns aligned to regulatory and business needs, ensuring auditability and traceability.

• Ensure data privacy, encryption, access controls, and disaster recovery are embedded into all platform and solution designs.
• Define patterns for secure data access (least privilege), tokenisation/masking where required, and environment…