Job Description
The Mid-Level Data Engineer is responsible for building, maintaining, and optimizing data pipelines that support data-driven operations and analytics. This role involves developing reliable ETL processes, integrating diverse data sources, and ensuring the availability and quality of data across the organization. The incumbent collaborates closely with senior engineers, analysts, and data scientists to enable seamless data accessibility, while continuously developing technical skills in modern data engineering practices and tools.
Key Responsibilities:
- Design, develop, and maintain efficient data pipelines and ETL processes to support business intelligence, analytics, and machine learning initiatives.
- Support the integration of multiple data sources into centralized systems (e.g., data warehouses, data lakes, or cloud platforms).
- Monitor data flows, identify bottlenecks, and troubleshoot issues to ensure the reliability and performance of data processes.
- Assist in data pipeline testing, validation, and quality checks to ensure accuracy and consistency of data outputs.
- Create and maintain documentation for data processes, workflows, and integration tasks to support transparency and knowledge sharing.
- Work closely with senior engineers, data scientists, and analysts to understand data requirements and support ongoing data initiatives.
- Stay updated on emerging data engineering tools, technologies, and best practices, applying new knowledge to improve processes and efficiency.
Requirements
- NQF Level 6 or higher tertiary qualification in an Information and Communication Technology (ICT) field (e.g., Computer Science, Information Systems, or Data Engineering).
- Cloud certification (AWS, Azure, or GCP) preferred.
- Minimum of 3 years experience in a Data Engineer or similar data-focused technical role.
- Hands-on experience with data pipelines, ETL tools, and data integration frameworks.
- Familiarity with SQL, Python, and cloud-based data solutions (e.g., AWS Glue, Azure Data Factory, Google BigQuery).
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