Description

  • Optimize SQL queries for performance tuning and develop data engineering solutions using Python, PySpark, and Scala.
  • Design, implement, and maintain scalable data pipelines in Azure Cloud for efficient data ingestion, transformation, and storage.
  • Leverage Azure services such as Azure Data Factory (ADF), Azure Blob Storage, Azure Data Flows, Azure Databricks, and Azure Key Vault to manage and process large datasets securely.
  • Develop and manage data warehousing solutions using Azure Data Lake Storage (ADLS), SAP S/4HANA, Teradata, and SQL-based databases to ensure optimized data structures and query performance.
  • Write and optimize complex SQL queries, views, and stored procedures for seamless data extraction, transformation, and loading (ETL/ELT).
  • Collaborate with teams to integrate SAP business processes and master data into Azure-based data solutions.
  • Utilize SAP HANA, BW/4HANA, Teradata, and other ETL/data ingestion tools to process and analyze enterprise-scale data.
  • Integrate multi-cloud services with on-premises technologies for a seamless hybrid cloud data architecture.
  • Design and implement data models and data warehousing strategies to support scalable, high-performance ETL/ELT pipelines.
  • Build and manage distributed data processing systems for large-scale data extraction, ingestion, and transformation.
  • Deploy, manage, and scale cloud-native applications using Azure Kubernetes Services (AKS) and containerized environments.
  • Utilize version control systems like GitHub and work with CI/CD deployment pipelines to automate and streamline data engineering workflows.
  • Develop and optimize Azure-based data pipelines using Azure Data Factory, Azure Databricks, and Python for big data processing and analytics.
  • Create and maintain dashboards and reports using business intelligence tools like Power BI to provide actionable insights

Key Skills
Education

Bachelor's degree

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