Ai data engineer
Summary
Build and maintain the data infrastructure behind a client's AI/ML initiatives in Benoni: designing scalable data pipelines, ETL/ELT processes, and data warehouse/data lake solutions, while ensuring data quality for data science teams. Core stack includes SQL, Python or Scala, Spark/Hadoop, and AWS/Azure/GCP cloud data services.
Our client is looking for a talented AI Data Engineer to build and manage the data infrastructure essential for their AI and machine learning initiatives. Based in Benoni , you will be responsible for designing, developing, and maintaining scalable data pipelines that collect, transform, and store data for model training and analysis. This role is vital for ensuring data quality, accessibility, and readiness for our data science teams, enabling them to build impactful AI solutions. Join us to shape the data foundation of our advanced AI endeavors and contribute to significant technological advancements.
Key Responsibilities Design, build, and optimize robust and scalable data pipelines for AI/ML applications. Develop ETL/ELT processes to ingest, clean, and transform large datasets from various sources. Implement data warehousing and data lake solutions to support data analysis and model training. Ensure data quality, integrity, and governance across all data platforms. Collaborate with data scientists and ML engineers to understand data requirements and provide efficient data access. Requirements Bachelor's degree in Computer Science, Engineering, or a related field. Proven experience as a Data Engineer, with a focus on building data pipelines for analytics or AI/ML. Proficiency in SQL and programming languages such as Python or Scala. Experience with big data technologies (e.g., Spark, Hadoop) and cloud data services (AWS, Azure, GCP). Familiarity with data warehousing concepts and data modeling techniques. Benefits Competitive salary and comprehensive benefits. Opportunity to work on exciting AI and data-driven projects in Benoni. Continuous learning and professional development opportunities. Collaborative team culture and a focus on innovation. Contribute to the core data infrastructure for AI advancements.