SayPro Job Description: Monitoring and Evaluation (M&E) Specialist – Data Management Learnership at SayPro

Position: Monitoring and Evaluation (M&E) Specialist – Data Management Learnership
Location: SayPro Headquarters
Department: Data Management
Reports To: Head of Data Management
Type: Full-Time

SayPro Role Overview

The M&E Specialist – Data Management Learnership at SayPro is a role that carries significant responsibility. You will be focused on developing and executing comprehensive data management strategies, ensuring data accuracy, quality, and security through rigorous testing, sampling, and review processes. Your role will involve managing data storage, integration, and compliance, while also supporting data analytics and visualization efforts. This position offers an opportunity to gain hands-on experience in data management and make a substantial contribution to the effective use of data within the organization.

SayPro Key Responsibilities

1. Data Testing
– Protocols: Develop and implement data testing protocols to ensure data accuracy and consistency.
– Validation: Use automated tools and manual checks to identify and correct data errors.
– Assessment: Conduct regular data quality assessments to ensure reliability and integrity.
– Documentation: Document testing processes and outcomes for future reference and compliance.

2. Data Sampling
– Methodologies: Design and implement sampling methodologies for data collection.
– Selection: Choose representative samples from large datasets for detailed analysis.
– Analysis: Ensure sampling techniques are statistically sound and unbiased, and analyse sample data to infer trends.
– Reporting: Report on sampling results and their implications for overall data quality.

3. Data Review
– Procedures: Establish review procedures for evaluating data completeness and accuracy.
– Periodic Reviews: Perform periodic reviews to identify inconsistencies or gaps in data.
– Coordination: Work with data collection teams to address and rectify identified issues.
– Documentation: Document review findings and actions taken to correct data errors.

4. Data Storage Management
– Policies: Develop and implement policies for data storage and management.
– Security: Ensure data is stored securely and complies with organisational and regulatory standards.
– Access Management: Manage data access permissions to protect sensitive information.
– Optimization: Optimize storage solutions to balance cost, accessibility, and security.
– Backup: Regularly back up data to prevent loss and ensure recovery capabilities.

5. Data Storage Review
– Audits: Conduct regular audits of data storage systems to ensure compliance with best practices.
– Effectiveness: Evaluate the effectiveness of storage solutions in meeting organizational needs.
– Capacity: Assess storage capacity and performance to accommodate growing data volumes.
– Improvements: Implement improvements based on review findings to enhance data management.

6. Data Security
– Measures: Implement and manage data security measures to protect against unauthorized access and breaches.
– Monitoring: Monitor and manage security protocols, including encryption and access controls.
– Audits: Conduct regular security audits to identify and address vulnerabilities.
– Training: Provide training on data security best practices to staff members.
– Policies: Develop and enforce data protection policies in accordance with legal requirements.

7. Data Integration
– Strategies: Develop strategies for integrating data from multiple sources and systems.
– Consistency: Ensure data consistency and accuracy during integration processes.
– Tools: Implement tools and techniques for seamless data merging and synchronization.
– Troubleshooting: Monitor and troubleshoot integration issues to maintain data integrity.
– Documentation: Document integration processes and outcomes for transparency and reference.

8. Data Quality Assurance
– Standards: Establish data quality standards and benchmarks for evaluation.
– Monitoring: Implement procedures for ongoing data quality monitoring and improvement.
– Cleansing: Conduct data cleansing activities to remove duplicates and correct inaccuracies.
– Reporting: Develop reports on data quality metrics and improvement efforts.

9. Data Documentation
– Comprehensive Documentation: Create detailed documentation for data management processes and protocols.
– Records: Maintain records of data sources, methodologies, and changes.
– Guides: Develop user guides and manuals for accessing and using data systems.
– Updates: Ensure documentation is regularly updated to reflect current practices.

10. Data Governance
– Frameworks: Develop and implement data governance frameworks to oversee data management practices.
– Roles: Define roles and responsibilities for data stewardship and oversight.
– Committees: Establish data governance committees or teams to review and guide data policies.
– Compliance: Monitor compliance with data governance policies and standards.

11. Data Access Control
– Policies: Develop access control policies to regulate who can view and modify data.
– Mechanisms: Implement authentication and authorization mechanisms to safeguard data.
– Permissions: Review and update access permissions regularly to reflect changes in roles.
– Monitoring: Monitor access logs to detect and respond to unauthorized access attempts.
– Training: Provide training on access control protocols to ensure proper data handling.

12. Data Archiving
– Procedures: Establish archiving procedures for long-term storage of historical data.
– Accessibility: Ensure archived data remains accessible and retrievable when needed.
– Policies: Implement archival policies that comply with legal and organizational requirements.
– Reviews: Regularly review and update archival practices to accommodate evolving needs.

13. Data Retrieval
– Procedures: Develop procedures for efficient data retrieval and access.
– Search Functions: Implement search and query functions to facilitate data extraction.
– User-Friendly: Ensure retrieval processes are user-friendly and meet organizational needs.
– Support: Provide support and training on data retrieval techniques and tools.

14. Data Analytics Support
– Preparation: Provide support for data analytics activities, including data preparation and cleaning.
– Collaboration: Collaborate with analysts to ensure data is suitable for analysis and reporting.
– Models: Assist in developing data models and visualizations based on M&E data.
– Documentation: Document analytics processes and support materials for future use.

15. Data Compliance
– Practices: Ensure data management practices comply with relevant regulations and standards.
– Updates: Monitor changes in data-related regulations and update practices accordingly.
– Audits: Conduct compliance audits and address any issues identified.
– Guidance: Guide regulatory requirements and best practices for data management.

16. Data Migration
– Planning: Plan and execute data migration projects when transitioning to new systems.
– Accuracy: Ensure data is accurately and securely transferred between systems.
– Testing: Test migrated data to confirm accuracy and completeness.
– Issues: Address any issues during migration and implement corrective measures.
– Documentation: Document migration processes and outcomes for future reference.

17. Data Visualization
– Techniques: Develop and implement data visualization techniques to enhance understanding of data.
– Creation: Create dashboards, charts, and graphs to represent key metrics and trends.
– Clarity: Ensure visualizations are clear, accurate, and aligned with reporting needs.
– Training: Provide training on using visualization tools and interpreting visual data.

18. Data Management Training
– Programs: Develop and deliver training programs on data management best practices.
– Content: Assess training needs and customize content for different user groups.
– Evaluation: Evaluate the effectiveness of training programs and make improvements as needed.
– Support: Provide ongoing support and resources to reinforce learning.

19. Data Performance Monitoring
– Monitoring: Monitor the performance of data management systems and processes.
– Metrics: Develop performance metrics to assess system efficiency and effectiveness.
– Issues: Address performance issues and implement improvements.
– Reporting: Report on data management performance and system health.

20. Data Management Strategy Development
– Strategies: Develop and update strategies for data management to support organisational goals.
– Alignment: Align data management strategies with broader organisational and M&E objectives.
– Adjustment: Evaluate and adjust strategies based on performance data and changing needs.
– Communication: Communicate data management strategies to stakeholders and ensure alignment.
– Implementation: Monitor the implementation of data management strategies and measure their impact.

SayPro Qualifications

– Bachelor’s degree in Data Science, Information Systems, Statistics, or a related field (Master’s preferred).
– Experience with data management, including data testing, sampling, and storage.
– Proficiency in data analysis tools, visualisation software, and database management systems.
– Knowledge of data security practices and compliance regulations.
– Strong organisational skills with attention to detail and accuracy.

SayPro Personal Attributes

– Analytical mindset with a problem-solving approach.
– Strong communication skills for documenting and presenting data findings.
– Ability to work independently and as part of a team.
– Proactive in identifying and addressing data-related issues.
– Commitment to continuous learning and professional development.

This job description overviews the essential responsibilities and qualifications for the M&E Specialist—Data Management Learnership at SayPro. It outlines the essential duties related to data management and emphasises the importance of accuracy, security, and effective data utilization in supporting the organisation’s goals.

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