Data Science & Enablement Lead

Whizants (Pty) Ltd.; Gauteng; On-site; ZAR 700 000 - 1 000 000; Full time

Job summary

A leading data solutions company in South Africa seeks a Data Science & Enablement Lead to operationalize data governance and manage master data. You will execute enterprise strategies while collaborating with various teams to build a scalable data ecosystem, utilizing platforms such as Microsoft Azure. Candidates should have over 8 years of experience in data management, proficiency in data integration tools, and solid project management skills. This role emphasizes the importance of data quality and compliance.

Qualifications

• 8–10+ years in data management, including MDM, governance, integration and quality initiatives.
• Experience in the full development lifecycle of a Data Lake in a mid to large enterprise.
• Deep knowledge of MDM best practices and AI / ML model building.

Responsibilities

• Execute enterprise data strategy aligned with transformation and compliance goals.
• Implement MDM framework across master data domains.
• Design and operate a central data lake according to best practices.

Skills

Data Management

Master Data Management (MDM)

Experience with Oracle E-Business Suite

Project Management

Python

SQL

Microsoft Azure

Cloud ETL

Data Integration

Education

Bachelor's degree in IT, Computer Science, Data Science, or related field

Tools

Microsoft Azure Data Factory

Kafka

Active Directory

Job description

About the Role

Our client is looking for a hands‑on Data Science & Enablement Lead to execute the organisation's data vision and priorities.

This role focuses on operationalising Master Data Management (MDM), data governance, data quality, architecture, and enabling AI, advanced analytics, and data science.

You will ensure that enterprise data is accurate, governed, and integrated to support business operations, compliance, and innovation.

This includes collaborating across Architecture, Operations, Risk, Governance, and IT to build a scalable, science‑ready data ecosystem, integrating data from systems such as Oracle E‑Business Suite.

Key Responsibilities

Enterprise Data Execution

• Execute enterprise data strategy aligned with transformation, compliance, and AI / data science goals.
• Champion data standards, governance processes, and quality metrics across functions.
• Lead governance forums and implement policies, standards, and procedures.
• Develop and maintain an enterprise data platform using Microsoft Azure, Microsoft Fabric, or similar.
• Enable business teams with structured, accessible, and trusted datasets.

Master Data Management (MDM)

• Implement MDM framework across master data domains (items, customers, suppliers, etc.).
• Harmonize master data for consistency across systems and support analytics / AI.
• Monitor and improve data quality in terms of completeness, accuracy, and timeliness.

Data Integration & Platform Development

• Design and operate a central data lake according to best practices.
• Lead data integration from systems, including Oracle E‑Business Suite and others.
• Design and implement secure data access controls using Active Directory groups, ensuring authorised data access.
• Ensure data is accessible, secure, classified, and ready for AI, analytics, and data science.

Governance, Stewardship & Quality

• Implement data ownership, stewardship, and accountability models.
• Support stakeholders in compliance with governance controls.
• Monitor data quality metrics and address issues within SLAs.
• Ensure data meets ethical standards and is suitable for automated and algorithmic use.

Cross‑Functional Delivery

• Collaborate with Operations, Supply Chain, and Data Science teams to deliver integrated, reliable data.
• Work with Risk & Governance teams to maintain compliance, audit, and AI‑risk policies.
• Enable analytics and data science by supporting structured, interoperable data pipelines.

Key Deliverables

• Secure & Scalable Data Lake: Implement and maintain a centralized repository for business‑critical data with AD / role‑based access.
• Integrated Data Pipelines: Implement reliable and secure pipelines from Oracle and other platforms.
• MDM Framework: Deploy MDM for critical entities (customers, products, vendors) to ensure data accuracy and consistency.
• AI / ML Enablement: Support the design and deployment of ML models for predictive analytics, anomaly detection, segmentation.
• Data Access Architecture: Implement security frameworks based on Active Directory and role‑based security.
• AI Infrastructure & Insights: Build infrastructure for model deployment, performance monitoring, and automated reporting.
• Team Collaboration: Lead efforts with business units to discover AI / data opportunities and refine models.

Qualifications & Experience

Essential

• 8–10+ years in data management, including MDM, governance, integration, and quality initiatives.
• Experience in the full development lifecycle of a Data Lake in a mid to large enterprise.
• Expertise in Oracle E‑Business Suite and integrating data from multiple systems.
• Deep knowledge of MDM best practices and AI / ML model building.
• Experience with Microsoft Azure, Microsoft Fabric, or similar platforms.
• Strong project management skills for complex data initiatives.
• Proficiency in Python, SQL, Spark, cloud ETL, and orchestration tools.
• Experience in designing secure AD / role‑based access control systems.
• Experience with streaming data integration tools like Apache Kafka or Azure Event Hubs.

Desirable

• Familiarity with data governance frameworks (e.g., DAMA, DCAM).
• Experience with data cataloging / metadata tools.
• Experience in regulated industries such as finance, healthcare, or manufacturing.
• Background in FMCG, manufacturing, retail, or other data‑intensive sectors.
• Education: Bachelor’s degree in IT, Computer Science, Data Science, or a related field. Postgraduate qualifications in data, analytics, or BI are advantageous.

Technical Competence

• Skilled in Azure Data Factory, Synapse, Purview, Microsoft Fabric, etc.
• Familiar with Oracle EBS data structures and integration approaches.
• Hands‑on experience with MDM tools, ETL platforms, and governance frameworks.
• Strong knowledge of AI / data science needs: labeling, versioning, ethics.
• Proficient in operationalizing analytics pipelines for BI, AI, and ML.
• Experienced with AD‑based security and role‑based access control.
• Skilled in real‑time data streaming solutions (e.g., Kafka, Event Hubs).

Key Competencies

• Strategic thinking and end‑to‑end solution ownership.
• Strong analytical and problem‑solving capability.
• Excellent communication and stakeholder engagement skills.
• Leadership and mentorship abilities.
• Results‑driven with a strong focus on quality and delivery.