- Pretoria
- Salary: (Negotiable)
- Job Type: Permanent
- Sectors: Government & Parastatals
- Reference: 78903
Vacancy Details
Employer: Unique Personnel
Job Purpose: The primary purpose of this role is to lead and implement a comprehensive Data Governance Program and establish a robust, future-proof Data Architecture across multi-cloud platforms. This role is crucial to revolutionizing CIPC's data management, enhancing data quality, streamlining analytics processes, and creating a centralized data hub, including a Data Marketplace, to foster a data-driven culture and enable advanced analytics across the organization.
Key performance areas
Data Architecture Strategy & Design:
Architectural Blueprint: Design and maintain the conceptual, logical, and physical data models, data dictionaries, and comprehensive data flow diagrams across the enterprise.
Strategic Alignment: Develop forward-looking data strategies for data warehousing, data mining, and data integration that align with CIPC's business objectives and technological roadmap.
Tool Evaluation & Selection: Evaluate, recommend, and select appropriate data management tools, technologies, and platforms to ensure optimal performance, scalability, and cost-efficiency.
Data Lakehouse Architecture:
Cloud Architecture: Design and implement a secure, scalable, and highly available Data Lakehouse architecture across multi-cloud platforms (including Azure, AWS, and GCP services like Azure Databricks, AWS Redshift, Google BigQuery, and respective storage solutions).
Data Pipeline Development: Establish robust, end-to-end data ingestion (batch and streaming) and ELT/ETL pipelines to incorporate diverse data sources into the Data Lakehouse effectively.
Analytics Enablement: Develop optimized data transformation, processing, and storage solutions tailored for advanced analytics, reporting, and data science workloads.
Data Governance & Management
Governance Frameworks: Define and establish comprehensive data management frameworks for Data Ingestion, Data Lake, Data Warehouse, Data Sharing, Data Analytics, and Data Quality.
Data Marketplace & Sharing: Design and implement a Data Marketplace architecture to facilitate governed data sharing and collaboration among internal stakeholders, fostering a data-driven culture.
Quality & Observability: Define and implement data quality monitoring, validation, and improvement processes, including developing data observability solutions to track data health, usage, lineage, and compliance across the entire data ecosystem.
Metadata Management: Utilize and champion Data Catalog tools for effective data discovery, metadata management, and lineage tracking.
Collaboration, Guidance & Compliance
Stakeholder Engagement: Collaborate proactively with IT, business leaders, and security teams to prioritize data requirements, define service level agreements (SLAs), and develop data governance policies and procedures.
Mentorship & Support: Provide technical guidance and support to Data Analysts, Data Engineers, and Data Scientists, ensuring the effective and responsible leveraging of data assets.
Regulatory Compliance: Ensure all data systems and processes comply with relevant regulations, standards, and security policies.
Key performance areas
Data Architecture Strategy & Design:
Architectural Blueprint: Design and maintain the conceptual, logical, and physical data models, data dictionaries, and comprehensive data flow diagrams across the enterprise.
Strategic Alignment: Develop forward-looking data strategies for data warehousing, data mining, and data integration that align with CIPC's business objectives and technological roadmap.
Tool Evaluation & Selection: Evaluate, recommend, and select appropriate data management tools, technologies, and platforms to ensure optimal performance, scalability, and cost-efficiency.
Data Lakehouse Architecture:
Cloud Architecture: Design and implement a secure, scalable, and highly available Data Lakehouse architecture across multi-cloud platforms (including Azure, AWS, and GCP services like Azure Databricks, AWS Redshift, Google BigQuery, and respective storage solutions).
Data Pipeline Development: Establish robust, end-to-end data ingestion (batch and streaming) and ELT/ETL pipelines to incorporate diverse data sources into the Data Lakehouse effectively.
Analytics Enablement: Develop optimized data transformation, processing, and storage solutions tailored for advanced analytics, reporting, and data science workloads.
Data Governance & Management
Governance Frameworks: Define and establish comprehensive data management frameworks for Data Ingestion, Data Lake, Data Warehouse, Data Sharing, Data Analytics, and Data Quality.
Data Marketplace & Sharing: Design and implement a Data Marketplace architecture to facilitate governed data sharing and collaboration among internal stakeholders, fostering a data-driven culture.
Quality & Observability: Define and implement data quality monitoring, validation, and improvement processes, including developing data observability solutions to track data health, usage, lineage, and compliance across the entire data ecosystem.
Metadata Management: Utilize and champion Data Catalog tools for effective data discovery, metadata management, and lineage tracking.
Collaboration, Guidance & Compliance
Stakeholder Engagement: Collaborate proactively with IT, business leaders, and security teams to prioritize data requirements, define service level agreements (SLAs), and develop data governance policies and procedures.
Mentorship & Support: Provide technical guidance and support to Data Analysts, Data Engineers, and Data Scientists, ensuring the effective and responsible leveraging of data assets.
Regulatory Compliance: Ensure all data systems and processes comply with relevant regulations, standards, and security policies.
Candidate Requirements
Required Minimum Education / TrainingCandidates must meet one of the following requirements:
Formal Education Pathway (Preferred):
Education: Bachelor's Degree / Advanced Diploma in Computer Science, Computer Engineering, Data Analytics, Data Engineering, Statistics/Mathematical Science/Economic Sciences, or a relevant quantitative field.
Required Minimum experience
Experience: Minimum 10–15 years of proven experience as a Data Architect, Solutions Architect, or equivalent senior data leadership role, with a strong focus on cloud-native data architecture and governance.
Cloud Platforms: Deep understanding of cloud services and technologies relevant to data management and analytics, including Azure, AWS, and Google Cloud.
Technical Skills: Strong knowledge of data modeling, ETL/ELT processes, data warehousing, and data governance best practices. Proficiency in SQL, Python, Spark, and other relevant programming languages and tools.
Specialized Certification and Experience Pathway (Alternative):
Required Minimum Education / Training
Education: National Diploma in Information Technology or a related technical field.
Required Minimum Experience
Experience: Minimum 12–17 years of progressive experience in Data Governance, Data cataloging tools, and Data Lakehouse implementation, Data Engineering, and Data Architecture, demonstrating a career trajectory that has built the equivalent strategic and leadership capabilities of a Bachelor's degree holder.
Mandatory Certifications: The candidate must hold a combination of at least two advanced professional certifications, which must include one strategic framework and one platform-specific architect certification:
Strategic Architecture/Governance (MUST include one):
TOGAF Certification (e.g., TOGAF 9 Certified or equivalent)
Certified Data Management Professional (CDMP) (at Professional or Master level)
Cloud Platform/Engineering (MUST include one):
Databricks Certified Data Engineer (Associate or Professional)
Microsoft Certified: Azure Solutions Architect Expert / Azure Data Engineer Associate
Google Cloud Certified Professional Data Engineer / Professional Cloud Architect
AWS Certified Solutions Architect – Professional / Data Analytics – Specialty
Minimum Functional Requirements (Technical Skills & Knowledge)
Deep Data Modeling Expertise: Expert-level proficiency in designing and implementing dimensional (Star/Snowflake), Data Vault, and Relational data models, with a clear understanding of the trade-offs between them.
Cloud Data Platform Mastery: In-depth knowledge and proven experience designing and deploying scalable data solutions using at least two major cloud platforms (Azure, AWS, or GCP), including their respective data warehousing, data lake, and compute services.
Big Data Ecosystem: Strong hands-on experience with Apache Spark (PySpark/Scala) for large-scale data processing and experience with modern messaging/streaming technologies (e.g., Kafka).
Data Governance: Proven ability to implement technical solutions for Metadata Management, Data Lineage, Data Quality (DQ), and Data Observability.
Advanced SQL & Programming: Expert proficiency in writing complex, optimized SQL queries and extensive experience in a programming language like Python or Scala for pipeline development.
Architecture Frameworks: Practical experience applying Enterprise Architecture methodologies and frameworks, such as TOGAF, to data initiatives.
Security & Access Control: Expertise in designing and enforcing fine-grained data access controls, encryption, and data masking techniques across cloud data stores.
Technical Skills: Strong knowledge of data modeling, ETL/ELT processes, data warehousing, and data governance best practices. Proficiency in SQL, Python, Spark, and other relevant programming languages and tools.
Soft Skills: Excellent communication, collaboration, and problem-solving skills. Ability to work independently and lead a team of technical resources.
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