Description

• Strong, recent hands-on expertise with Azure Data Factory and Synapse is a must (3+ years).
• Strong expertise in designing and implementing data models, including conceptual, logical, and physical data models, to support efficient data storage and retrieval.
• Strong knowledge of Microsoft Azure, including Azure Data Lake Storage, Azure Synapse Analytics, Azure Data Factory, and Azure Databricks, pySpark for building scalable and reliable data solutions.
• Extensive experience with building robust and scalable ETL/ELT pipelines to extract, transform, and load data from various sources into data lakes or data warehouses.
• Ability to integrate data from disparate sources, including databases, APIs, and external data providers, using appropriate techniques such as API integration or message queuing.
• Proficiency in designing and implementing data warehousing solutions (dimensional modeling, star schemas, Data Mesh, Data/Delta Lakehouse, Data Vault)
• Proficiency in SQL to perform complex queries, data transformations, and performance tuning on cloud-based data storages.
• Experience integrating metadata and governance processes into cloud-based data platforms
• Certification in Azure, Databricks, or other relevant technologies is an added advantage
• Experience with cloud-based analytical databases.
• Experience with Azure MI, Azure Database for Postgres, Azure Cosmos DB, Azure Analysis Services, and Informix.
• Experience with Python and Python-based ETL tools.
• Experience with shell scripting in Bash, Unix or windows shell is preferable.
• Demonstrated ability to lead cross-functional engineering teams, define technical strategy and architecture, drive delivery of complex data platforms, mentor engineers, and effectively communicate with stakeholders at all organizational levels

Key Skills

Education

Bachelor's degree