Senior Data Architect

DESCRIBE THE JOB PROFILE SUMMARY OR PURPOSE OF THE REQUESTED JOB The key focus for the senior data architect is to perform planning aligned to key data solutions, build and participate the architecture capability building, perform data architecture and design, manage data architecture risk and compliance, provide design and build governance and support and communicate and share knowledge around the architecture practices, guardrails, blueprints and standards related to the data solution design. DESCRIBE THE MAIN ACTIVITIES OF THE JOB (DESCRIPTION)

Planning

• Lead data solution requirements gathering and ensure alignment with business objectives and constraints.
• Define and refine data architecture runways for intentional architecture with the key stakeholders
• Provide input into business cases and costing
• Participate and provide data architectural runway requirements into Programme Increment (PI) Planning

 

Architecture Capability

• Develop and oversee data architecture views and ensure alignment with enterprise architecture.
• Maintain and oversee the data solution artifacts in the set enterprise repository and knowledge portals aligned to the rest of the architecture
• Manage the data architecture processes based on the requirements for each architype
• Manage change impact of the data architecture with stakeholders
• Develop and participate in the build of the data architecture practice with embedded architects and engineers including the relevant methods, repository and tools
• Manage the data architecture considering the business, application, information/data and technology viewpoints
• Establish, enforce and implement data standards, guardrails, frameworks, and patterns

 

Solution Design

• Lead and review logical and detailed data architecture
• Evaluate and approve data solution options and technology selections
• Select appropriate technology, tools and build for the solution
• Oversee and maintain the data solution blueprints
• Drive incremental modernisation initiatives in the delivery area

 

 

Risk, Governance and Compliance

• Identify, assess and mitigate risks at a data solution architecture level
• Ensure and enforce compliance with policies, standards, and regulations
• Lead data architecture reviews and integrate with governance functions
• Integrate with other governance and compliance functions to ensure continuity in managing the investment and risk for the organisation pertaining to the solution architectures
• Establish and provide data standards, guidance, and tools to delivery teams.

 

Implementation and Collaboration

• Establish and provide data solution architectures and tools to the delivery and data engineering teams 
• Lead and facilitate collaboration with delivery teams to achieve architecture objectives
• Manage and resolve deviations and ensure up-to-date data solution design documentation
• Identify opportunities to optimise delivery of solutions
• Oversee and conduct post-implementation reviews
• Ensure the data architecture supports CI/CD pipelines to facilitate rapid and reliable deployment of data solutions
• Implement automated testing frameworks for data solutions to ensure quality and reliability throughout the development lifecycle.
• Establish performance monitoring and optimisation practices to ensure data solutions meet performance benchmarks and can scale as needed.
• Integrate robust data security measures, including encryption, access controls, and regular security audits, into the implementation process.

 

Communication and Knowledge Sharing

• Communicate and advocate up-to-date data solution architecture views
• Communicate the relevant data standards, practices, guardrails and tools to stakeholders relevant to the solution design
• Ensure IT teams are well-informed and trained in architecture requirements
• Communicate and collaborate with stakeholders' relevant views on planning, technology assessments, risk, compliance, governance and implementation assessments
• Foster collaboration between data architects, data engineers, and other IT teams through regular cross-functional meetings and agile ceremonies.
• Communicate and maintain up-to-date blueprint designs for key data solutions
• Ensure effective participation in the agile ceremonies (PI planning, sprint planning, retrospectives, demos)
• Implement regular feedback loops with stakeholders and end-users to continuously improve data solutions based on real-world usage and requirements
• Create a culture of knowledge sharing by organising regular workshops, training sessions, and documentation updates to keep all team members informed about the latest data architecture practices and tools

 

MINIMUM QUALIFICATIONS/EXPERIENCE (REQUIRED FOR THE JOB)

• Matric 
• Degree or diploma in Information Technology, Computer Science, Engineering OR relevant diploma / degree
• Experience: Requires a minimum of 5 years in a technical/solution design role and a minimum of 7 years relevant IT experience
• Data Experience: Required a minimum of 7 years related experience in data engineering, data modeling and design and data management and governance

 

Data Related Experience:

• Big Data and Analytics (e.g., Hadoop, Spark)
• Data Warehousing (e.g., DataBricks, Snowflake, Redshift)
• Master Data Management (MDM)
• Data Lakes and Data Mesh
• Metadata Management
• ETL/ELT Processes
• Data Privacy and Compliance Cloud Data Services

 

ADDITIONAL QUALIFICATIONS/EXPERIENCE (PREFERRED)

• DAMA-DMBOK
• TOGAF
• ArchiMate
• Cloud Certifications (AWS, Azure)
• Financial Industry Experience

 

 

 

COMPETENCIES REQUIRED

Related attributes and competencies related to architecture:

• Critical thinking/problem solving
• Teamwork/collaboration
• Effective Communication Skills
• Leadership skills
• Knowledge and experience in architecture domains
• Knowledge and experience in architecture methods, frameworks and tools
• Solution Design Experience
• Agile Knowledge and Experience
• Cloud Knowledge and Experience

 

Data related competencies:

• Data modeling, database design and data governance best practices and implementation
• Data architecture principles and methodologies
• Data integration technologies and tools
• Data management and governance