Skills

Full description

Design, build, and maintain real-time data streaming and ETL pipelines.
Strong hands-on experience with Spark, PySpark, Hive, SQL, Python, and RDBMS.
Experience with Azure Cloud and Databricks for data engineering and analytics workloads.
Build and optimize data pipelines using Spark, SQL, and Python, including SQL query tuning.
Minimum 2 years of hands-on experience in Machine Learning, including data preparation, feature engineering, model development, and ML pipelines.
Exposure to integrating data pipelines with Machine Learning models and ML workflows.
Experience with Unix/Linux shell scripting.
Knowledge of Kafka, Kubernetes, CI/CD, and cloud platforms such as Azure/AWS/GCP is an advantage.
Follow data integration best practices and ensure data quality and integrity.
Collaborate with QA teams on system integration testing and support Agile delivery.
Deploy and maintain ETL/data pipeline code and contribute to task planning and team coordination.

Design, build, and maintain real-time data streaming and ETL pipelines.
Strong hands-on experience with Spark, PySpark, Hive, SQL, Python, and RDBMS.
Experience with Azure Cloud and Databricks for data engineering and analytics workloads.
Build and optimize data pipelines using Spark, SQL, and Python, including SQL query tuning.
Minimum 2 years of hands-on experience in Machine Learning, including data preparation, feature engineering, model development, and ML pipelines.
Exposure to integrating data pipelines with Machine Learning models and ML workflows.
Experience with Unix/Linux shell scripting.
Knowledge of Kafka, Kubernetes, CI/CD, and cloud platforms such as Azure/AWS/GCP is an advantage.
Follow data integration best practices and ensure data quality and integrity.
Collaborate with QA teams on system integration testing and support Agile delivery.
Deploy and maintain ETL/data pipeline code and contribute to task planning and team coordination.