Senior Data Engineer to cloud Data Warehouse maintenance and...

Aurora Cannabis Enterprises Inc. Calgary, AB 2022-02-18
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Our client is seeking a Senior Data Engineer to support cloud base Data Warehouse maintenance and modernization. Mandatory


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.


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