Data Engineer at SNDL
Toronto, ON M5V 2J1, Canada -
Full Time


Start Date

Immediate

Expiry Date

23 Nov, 25

Salary

0.0

Posted On

23 Aug, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Sql Server, Data Engineering, Data Models, Data Solutions, Dax, Data Integration, Business Intelligence, Data Security, Master Data Management, Data Architecture, Communication Skills, Data Services, Analytics, Internal Controls, Operations, Python

Industry

Information Technology/IT

Description

EXPERIENCE AND QUALIFICATIONS:

  • Minimum of 5 years in data engineering, data integration, and data platform development roles.
  • Expert level knowledge and use of Microsoft Azure data services, including Azure Synapse, Azure Data Factory, Microsoft Fabric, SQL Server, and Azure SQL.
  • Proven ability to design, implement and optimize data warehouses with industry-standard frameworks (e.g. Kimball).
  • A comprehensive understanding and application of modern data architecture such as fabric or mesh architecture.
  • Proven ability to design and implement enterprise-grade data pipelines and dimensional data models for business intelligence and analytics.
  • Expert level knowledge of Power BI dataset development, DAX, and Power Query.
  • Advanced SQL skills are required for this role; the ability to create, debug and optimize SQL Server stored procedures is a key requirement.
  • Programming experience with “data engineering” languages such as Python is an asset.
  • Experience implementing CI/CD pipelines using Azure DevOps for deploying data integration solutions in a controlled, auditable, and automated manner.
  • Familiarity with internal controls and SOX-compliance related to enterprise data management, role-based access controls, and data security is preferred.
  • Experience extracting and transforming data from ERP systems, preferably Dynamics 365 Finance and Operations.
  • Familiarity with data governance frameworks is an asset.
  • Knowledge of, and experience with, master data management is preferred.
  • Prior experience in the retail industry is preferred.
  • Proven ability to work in cross-functional teams of ERP analysts, software developers, and business analysts, to deliver integrated and business-aligned data solutions.
  • Exceptional verbal and written communication skills; the ability to clearly describe and document data flows, data models, ETL, and data integration processes.
  • Preferred certifications include Microsoft Certified Azure Data Engineer Associate, Power BI Data Analyst Associate, Fabric Data Engineer Associate, and/or DevOps Engineer.
Responsibilities
  • Design, develop, and maintain scalable data integration pipelines with Microsoft tools such as Azure Synapse Analytics, Azure Data Factory, Microsoft Fabric, and SQL Server.
  • Build and optimize semantic data models and dimensional schemas to support analytics and reporting across business units and functions including sales, operations, supply chain, customer engagement, finance, HR and other analytics consumers.
  • Extract, transform, and load (ETL/ELT) data from Microsoft Dynamics 365 Finance and Operations into the enterprise data platform.
  • Integrate data from other line-of-business applications to support analytics across the organization.
  • Build, publish and manage curated datasets in Power BI, ensuring alignment with reporting standards and analytical needs.
  • Implement, manage and mature CI/CD pipelines for data integration workflows using Azure DevOps, enabling consistent, automated, and testable deployments across environments that enable complete data lineage tracing from data-source to report.
  • Work closely with the ERP and application teams, application developers, and business analysts to understand and fully document data sources, application data flows, integration requirements, and business context for analytics.
  • Proactively identify, investigate, and resolve issues in data pipelines (and data repositories) to ensure high availability, quality, and reliability of data provisioning processes.
  • Contribute to the development and deployment of enterprise-wide data models (e.g. the customer entity) and business-centric data glossaries that mature the organization’s data literacy and adoption of data and analytics for decision-making.
  • Monitor, troubleshoot, and optimize data integration processes, stored procedures and other data processes enabling analytics and enterprise data integration
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