Senior Data Engineer - AI Focus
Summary
Senior data engineer in Sandton (hybrid) who designs, builds, and maintains scalable data pipelines and infrastructure for AI/ML applications, ensuring clean, secure, well-governed data for model training. Core tech: SQL/NoSQL, data warehousing, Spark/Hadoop, cloud platforms (AWS/Azure/GCP), and pipeline orchestration tools.
About the Role
Our client is seeking an experienced Senior Data Engineer with a strong focus on AI and Machine Learning to join their team in Sandton . In this hybrid role, you will be instrumental in designing, building, and maintaining scalable data pipelines and infrastructure that power our advanced AI initiatives. You will work closely with data scientists and AI researchers to ensure data is clean, accessible, and optimized for model training and deployment. This is an excellent opportunity for a data engineering expert passionate about enabling cutting-edge AI solutions and contributing to data-driven innovation within a dynamic environment.
Key Responsibilities
- Design, construct, install, test, and maintain highly scalable data management systems and pipelines for AI/ML applications.
- Develop and optimize data ingestion, transformation, and storage processes using big data technologies.
- Collaborate with data scientists and AI engineers to understand their data requirements and provide robust data solutions.
- Ensure data quality, integrity, and security across all data platforms.
- Implement data governance and best practices for data management in an AI context.
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.
- 5+ years of experience in data engineering, with a proven track record in building data infrastructure for AI/ML.
- Strong proficiency in SQL and NoSQL databases, data warehousing, and big data technologies (e.g., Spark, Hadoop).
- Experience with cloud data platforms (AWS, Azure, GCP) and data pipeline orchestration tools.
- Familiarity with machine learning concepts and the data requirements for AI model development.
Benefits
- Competitive salary and performance-based bonuses.
- Hybrid work model providing a balance of in-office and remote work.
- Comprehensive health, dental, and retirement benefits.
- Opportunities for professional development, certifications, and attending conferences.
- A collaborative and innovative workplace culture.