- Data Engineering : Proficient in designing and implementing ETL/ELT processes using SQL Server and cloud-based tools, with a preference for Azure Data Factory, Synapse, and Microsoft Fabric.

- Data platforms : Strong experience with data platforms such as Microsoft Fabric, Azure Data Gateway, Azure Synapse, or Snowflake and Databricks.

- Programming : Expertise in Python, T-SQL, Spark, or other scripting languages for data transformation.

- Data Quality : Strong understanding of data quality checks and best practices.

- Communication : Excellent verbal and written communication skills, with the ability to convey technical information in business terms.

- Methodologies : Experience with Agile/Scrum project delivery is a plus.

Good to Have :

- Certifications : Microsoft certifications in Azure or related technologies are a plus.

- Tools : Experience with Power BI and integration with Azure cloud data platforms, particularly Microsoft Fabric.

Data Pipeline Development :

- Design, develop, and optimize ETL/ELT processes using SQL Server, Azure Data Factory, Synapse, Snowflake, or equivalent tools.

- Build and maintain data pipelines to support real-time and batch processing needs, with a preference for Azure Data Stack, especially Microsoft Fabric.

- Implement data transformations, cleansing, and quality checks to ensure data integrity and reliability.

Cloud Integration & Management :

- Integrate on-premises data sources with cloud-based solutions using Azure services such as Azure Data Factory, ODG(On prem data Gateway), Synapse, Azure Fabrics, and Snowflake, as well as AWS when necessary.

- Manage and maintain data lakes, lakehouses, Microsoft Fabric, and data warehouses in cloud environments.

- Ensure data availability, lineage, and performance across all data platforms with a focus on Azure technologies.

Collaboration & Support :

- Work closely with data architects, analysts, and BI developers to implement data solutions that meet business requirements, emphasizing Azure Data Stack capabilities and Snowflake integration.

- Collaborate with stakeholders to translate business needs into technical solutions.

Monitoring & Optimization :

- Monitor data pipeline performance and troubleshoot issues to ensure optimal operation, leveraging Azure monitoring tools capabilities.

- Optimize data storage and retrieval mechanisms for enhanced system performance, focusing on Azure technologies.

- Maintain comprehensive documentation of data workflows, processes, and system architecture.