Responsibilities Databricks Expertise: The most critical skill is extensive experience with Databricks, covering data processes like ETL (Extract, Transform, Load) and data migration. Having Databricks certifications is a strong preference.Machine Learning (ML) Operations: The role requires a strong background in implementing and optimizing machine learning workflows. This includes using specialized tools like Databricks ML, MLflow, and Spark MLlib to manage the full lifecycle of a model, from data preparation to deployment.Data Management: The candidate needs a solid understanding of data warehousing, data modeling, and database design. Experience with both traditional relational databases (RDBMS) and migrating data to modern cloud platforms is essential.Cloud Computing: Knowledge of major cloud platforms such as AWS, Azure, or GCP is required, as Databricks is a cloud-based service.Programming: Proficiency in key languages for data science and engineering, including Python and SQL, is a must.Data Governance: The role requires knowledge of best practices for data governance, data quality management, and data security. Technologies and Tools The list mentions specific technologies that the ideal candidate should be familiar with:Platforms: Databricks, AWS, Azure, GCP.Programming Languages: Python, SQL, and other scripting languages.Databases: Relational databases like SQL Server.ML Libraries: scikit-learn, Spark MLlib.MLOps Tools: MLflow.Containerization: Docker and Kubernetes.
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