Senior Data Engineer to support cloud base Data Warehouse...

Aurora Cannabis Enterprises Inc. Calgary, AB 2022-02-13
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Our client is seeking a Senior Data Engineer to support cloud base Data Warehouse maintenance and modernization.

Mandatory Skills:

• Very strong experience in database systems including data warehouses and multi-dimensional analytic solutions.

• Deep understanding of Azure cloud technologies and infrastructure for data retention and processing such as Microsoft Azure Data Lake, Functions, Data Factory, Logic Apps, Azure Synapse and Databricks.

• Proficiency in scripting and programming using R and/or Python, C#.

• Outstanding skills in Microsoft full BI Stack in Azure environment.

• Excellent SQL development skills to write complex queries involving multiple tables and develop stored procedures, triggers, user defined functions.

• Proficiency in Extracting, Transforming and Loading (ETL) data between data systems/ data warehouses. Experience in ETL change data capture methodologies and techniques, ETL data quality validation for mission critical data migrations.

• Experience in managing and deploying code to cloud services, including Azure DevOps experience.

• Working knowledge of Linux, Visual Studio.

• Hand on experience with deployment and monitoring of machine learning models.

• Hand on experience with Microsoft Power Suite – Power Apps and Power BI.

• Knowledge in statistics and data mining.

• Strong Agile project execution experience with proven ability to prioritize and manage time. RESPONSIBILITIES:

• Maintain cloud base enterprise data warehouse and data marts.

• Automate reporting solutions and operationalize machine learning models.

• Implement optimized pipelines to extract and merge data sets from multiple sources.

• Perform data cleansing and transformation using advanced Azure tools.

• Work with the team to deploy new cloud-based reporting and analytics tools.

• Work side-by-side with the business SME's, iterate the development in an agile fashion to build and test solutions with incremental changes.

• Set up automated deployment processes.

• Implement data process improvements such as optimizing data delivery and scalability