Architect, Data Solutions
Financial Services, Banking
Minimum Requirements
- Relevant Architecture Certification;
- AWS Certification advantageous
- TOGAF Frameworks
- Adopting Practical Approaches
- Articulating Information
- Challenging Ideas
- Checking Things
- Examining Information
- Exploring Possibilities
- Interacting with People
- Meeting Timescales
- Producing Output
- Providing Insights
- Taking Action
- Team Working
- Data Integrity
- IT Applications
- Knowledge Classification
- Knowledge Management Systems
- Systems Design
Responsibilities
- Design scalable, secure, and high-performance data solutions aligned with business requirements.
- Define data architecture standards, patterns, and best practices.
- Lead the selection of appropriate technologies, platforms, and tools for data solutions.
- Contribute to the development and execution of enterprise data strategies.
- Align data architecture with business goals, digital transformation initiatives, and regulatory requirements.
- Promote data as a strategic asset across the organization.
- Develop conceptual, logical, and physical data models.
- Architect data integration solutions across on-premises and cloud environments.
- Ensure data consistency, quality, and lineage across systems.
- Design and implement cloud-native data solutions (e.g., Azure, AWS, GCP).
- Evaluate and integrate data platforms such as data lakes, data warehouses, and lakehouses.
- Optimize data storage, compute, and processing architectures.
- Embed data governance principles into solution design.
- Ensure compliance with data privacy regulations (e.g., POPIA, GDPR).
- Implement data security controls, access management, and encryption strategies.
- Work closely with business units, data engineers, analysts, and IT teams.
- Translate business needs into technical requirements and data solutions.
- Present architectural decisions and roadmaps to senior leadership.
- Stay abreast of emerging technologies and trends in data architecture.
- Drive innovation in data engineering, analytics, and AI/ML enablement.
- Continuously improve architecture for performance, cost-efficiency, and agility.
- Provide technical leadership and mentorship to data engineering and analytics teams.
- Establish architectural review processes and promote knowledge sharing.
- Contribute to talent development and capability building in data disciplines.
