Data Engineer (Azure)

Experience/knowledge

• At least 5 yrs of experience in Data Engineering
• Proficient in Azure, T-SQL, PowerShell, SSIS, SSAS & Power BI
• In-depth understanding of MDS, DBMS, OLAP & ETL frameworks
• Working knowledge of using the APIs and other data exchange methods used in enterprises
• Background in data warehouse design & SSAS tabular cube data modelling
• Visualization tools such as Power BI, Tableau, Qlik or React.

Key Accountabilities

• Design, develop, and maintain data pipelines, databases, and analytical solutions that align with business and technical requirements.
• Develop data integrations, data warehousing, and reporting solutions to ensure the delivery of accurate, consistent, and timely data for business intelligence (BI) and analytics.
• Integrate data from various source systems and facilitate data flows to downstream systems using ETL processes and APIs.
• Conceptualize, design, and produce professional BI reporting solutions using reporting tools, applications, and queries or scripts.
• Design, develop, and maintain data models in operational data stores and data warehouses to support data quality and performance.
• Configure and support MDS models and entities
• Work with Azure DevOps to manage and maintain code repositories as required by the development activities within the data team.
• Design and support data models/solutions enabling data-based automation for RPA processes.
• Review database changes and ongoing scheduled processes to ensure optimal performance and adherence to best practices.
• Provide technical input during data schema and database design workshops with business and technical stakeholders.
• Manage the deployment of changes to the SQL Server/Cloud production environment, ensuring that robust change control processes are followed.
• Design, deploy, and support data solutions that use Event Driven Architecture in Azure.
• Manage the deployment of changes to the Azure EDA environment using CI/CD Azure automated pipelines.
• Enable the firm's data strategy by adopting new data platforms such as MS Fabric.
• Ensure that technical project delivery meets high standards of security, reliability, and scalability.
• Work with various stakeholders to understand their data requirements and provide them with reliable, user-friendly, and scalable solutions.
• Collaborate with other data professionals to ensure data quality, consistency, and security across the data ecosystem