Data Architect MMH260310-9, Cape Town

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 optimised, 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.

Requirements

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)

Knowledge

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

Duties and Responsibilities Data Architecture Leadership (Enterprise&Domain)

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.

Modern Cloud Data Platform Architecture (AWS‑first; Hybrid‑capable)

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.

Data Engineering Enablement&Delivery Assurance (Hands‑on Architectural Accountability)

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 non‑functional 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).

Data Governance, Metadata, Lineage&Quality (Operationalised)

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.

Security, Privacy, Resilience&Compliance by Design

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 separation across dev/test/prod and sensitive workloads.

Drive security and risk requirements into solution designs early (threat modelling, compliance checks, operational controls).

AI / ML&Advanced Analytics Enablement The Data Architect ensures the modern data platform supports AI/ML and advanced analytics requirements, including:

Design patterns for AI/ML training datasets vs inference/serving datasets (latency, freshness, and reliability requirements).

Enable feature reuse and consistent feature definitions (feature management patterns; feature store concepts where relevant).

Ensure high‑quality curated datasets (golden datasets / data products) suitable for analytics and ML, with clear ownership and SLAs.

Architect for unstructured and semi‑structured data (documents, text, images, transcripts), including search‑ready and analytics‑ready representations.

Support modern AI data needs such as embeddings/vector representations, metadata enrichment, and retrieval patterns where applicable.

Ensure reproducibility and traceability for AI/ML by strengthening lineage, dataset versioning patterns, and auditability of derived datasets.

Work closely with analytics and ML stakeholders to ensure platform capability meets experimentation, deployment, monitoring, and governance needs.

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