Responsibilities
Data engineering and data management:
• Design, develop, and maintain scalable data pipelines and architectures to support business and analytical requirements.
• Build and optimize databases, data warehouses, and data lakes for performance, reliability, and scalability.
• Extract, transform, and load (ETL/ELT) data from a variety of structured and unstructured sources.
• Develop and implement automated processes for data acquisition, integration, cleansing, transformation, and storage.
• Define and implement data storage solutions based on business and technical requirements.
• Create and maintain physical and logical data models to support enterprise data initiatives.
• Support data migrations across multiple databases, servers, and cloud platforms.
• Ensure data quality, consistency, integrity, and governance across all data assets.
Analytics and machine learning support:
• Collaborate with Data Scientists and Analytics teams to operationalize machine learning models.
data workloads.
• Deploy machine learning models into production environments.
• Build robust testing frameworks to validate data transformations and model outputs.
Data architecture and solutions development:
• Design and implement secure, scalable, and highly available data platforms.
• Evaluate and recommend data solutions, technologies, and tools to address business requirements.
• Develop databases optimized for analytics, reporting, and advanced data processing.
• Build and manage cloud-based data solutions utilizing modern cloud technologies.
Quality assurance and change management:
• Perform root-cause analysis and troubleshooting for data-related issues.
• Design and execute testing scenarios to validate data quality and transformation accuracy.
• Assess, document, and implement changes in accordance with change management processes.
• Ensure release processes, procedures, and documentation are maintained and updated.
• Support configuration management and release management activities.
Stakeholder engagement:
• Collaborate with business, analytics, and technical teams to improve data accessibility and usability.
• Translate business requirements into technical data solutions.
• Provide recommendations regarding data architecture, integration, and optimization strategies.
• Communicate complex technical concepts effectively to both technical and non-technical stakeholders.
Skills:
• Strong programming skills in Python
• Advanced SQL development skills, including:
• SQL Server
• MySQL
• Relational database technologies
• ETL/ELT processes
• Data pipelines
• Data warehouses
• Data lakes
• Experience working with large-scale structured and unstructured datasets.
• Knowledge of data governance, data quality, and metadata management practices.
• Experience building and maintaining highly available and scalable systems.
• Understanding of cloud platforms such as:
• Microsoft Azure
• Amazon Web Services (AWS)
• Google Cloud Platform (GCP)
• Hadoop
• Cassandra
• Storm
• Similar distributed processing frameworks
• Python
• Bash
• Shell Scripting
• Perl
Analytics and reporting:
• Working knowledge of Power BI for dashboards, reports, and analytical solutions.
• Understanding of:
o Data visualization
o Data virtualization
o Augmented analytics
o Business intelligence solutions
Qualifications:
• Matric and a Bachelor’s Degree in Computer Science, Information Technology
• Certified Data Engineer certification.
• Cloud certifications (Azure, AWS, Google Cloud).
• Professional data and analytics certifications.
Experience:
• Proven experience in a Data Engineering role within a fast-paced technology environment.
• Experience developing modern data and analytics platforms that deliver actionable insights from large and complex datasets.
• Strong hands-on experience with Python development.
• Experience designing and supporting ETL/ELT frameworks and data integration solutions.
• Experience working with SQL Server, MySQL, and enterprise database solutions.
• Experience with cloud technologies, including SaaS, PaaS, and IaaS environments.
• Experience with automation, scripting, and data process orchestration.
• Experience building secure, scalable, resilient, and highly available data platforms.
• Experience supporting machine learning and advanced analytics initiatives.
• Experience with big data technologies and distributed computing environments.