Ai data engineer
Summary
Designs, builds, and maintains scalable data pipelines and ETL/ELT processes that feed AI/ML initiatives, ensuring data quality and performance for large datasets. Works with SQL/NoSQL, big data tools like Spark and Hadoop, cloud data services, and Python or Scala in a hybrid team supporting data scientists and ML engineers.
Our client is seeking a skilled AI Data Engineer to join their data-centric team in Kimberley. This role is crucial for building and maintaining the robust data infrastructure that fuels their artificial intelligence and machine learning initiatives. You will be responsible for designing, developing, and optimizing data pipelines, ensuring the availability, quality, and accessibility of data required for model training and analysis. This is an exciting opportunity for a data professional passionate about AI to work with large datasets and cutting-edge technologies, enabling data-driven insights and advanced predictive capabilities within a collaborative hybrid work setting.
Key Responsibilities Design, build, and maintain scalable and efficient data pipelines for AI/ML applications. Develop ETL/ELT processes to ingest, transform, and prepare data from various sources. Ensure data quality, integrity, and consistency across all data systems. Optimize data storage and query performance for large-scale datasets. Collaborate with data scientists and ML engineers to understand data requirements and provide necessary datasets. Implement data governance and security best practices. Requirements Bachelor's degree in Computer Science, Engineering, or a related quantitative field. 3+ years of experience in data engineering, with a focus on supporting AI/ML projects. Proficiency in SQL and experience with relational and No SQL databases. Experience with big data technologies such as Apache Spark, Hadoop, or similar. Familiarity with cloud data services (e.g., AWS Glue, Azure Data Factory, GCP Dataflow). Programming skills in Python or Scala for data manipulation and pipeline development. Benefits Competitive salary and annual bonus. Hybrid work model offering flexibility between office and remote work. Comprehensive health, dental, and vision insurance. Opportunities for training and professional development in data engineering and AI. Access to modern data infrastructure and tools. A dynamic team environment focused on data innovation and collaboration.