📋 Description

• Build scalable data pipelines using distributed processing to support ingestion, transformation
• Organize data with Delta Lake and Unity Catalog for governance.
• Design an automated MLOps pipeline on Databricks and GCP covering the ML lifecycle.
• Prepare data, engineer features, train and validate models, register, deploy, and monitor them.
• Participate in discovery activities and migrate models in waves based on criticality.
• Collaborate with engineering and data teams; work within an agile delivery model.

🎯 Requirements

• Experience with Databricks and modern data engineering environments.
• Hands-on PySpark and Apache Spark for distributed processing.
• Experience building workflows with Apache Airflow.
• Practical experience with Google BigQuery and cloud-based data platforms.
• Experience using MLflow for ML lifecycle management.
• Knowledge of AWS Glue, SQL and NoSQL databases (PostgreSQL, MongoDB, Cassandra).

🎁 Benefits

• Opportunity to work with modern data engineering, AI, cloud, and MLOps tech.
• Exposure to large-scale Databricks and GCP environments.
• Contribute to end-to-end ML lifecycle automation and reusable solutions.
• Collaborative and agile work environment with continuous learning.
• Exposure to AI trend areas like Generative AI and emerging tech.
• Career growth in a technology-driven organization.