Data Engineer (Canada)

360training•Calgary, AB

About The Position

We are seeking an experienced and skilled Data Engineer to join our innovative and fast-paced team. This role is vital in designing, developing, and optimizing complex data architectures that support our business needs. With deep technical expertise in Relational Database systems and MPP warehouses like Snowflake, this role will mainly focus on Data Modeling and Data Warehouse Development. This role will also take on other data engineering tasks like managing and enhancing data integration and data visualization. Your experience across a wide range of data technologies and data development methodologies will enable you to deliver high-performance data solutions and drive the evolution of our data ecosystem to support critical business objectives. This role is ideal for a seasoned professional who thrives in solving complex data challenges and implementing the latest innovations in data engineering.

Requirements

3+ years of experience in data engineering with a strong focus on SQL Server, Azure Data Factory, and Snowflake
Proficient in SQL development, including stored procedures, query optimization, and performance tuning.
Experience with ETL development and data integration from a range of sources including SQL, DB2, Mysql, Azure Blob, and APIs.
Expertise in dimensional modeling, with practical knowledge of Kimbal methodologies
Hands-on experience with designing and optimizing data models.
Experience with Agile methodologies and tools like Atlassian JIRA for managing tasks and workflows.
Proficiency with version control systems such as Git is required

Nice To Haves

Good to have experience with SSRS and the ability to modernize legacy reporting by transitioning to Power BI.
deployment automation tools for CI/CD is a plus.

Responsibilities

Design, develop, and optimize scalable data pipelines, using Azure Data Factory, with on-prem (relational and semi-structured) data sources along cloud MPP solutions
Data Warehouse development, following industry best practices like Medallion Architecture, Dimensional modeling, Kimbal, Data Vault 2.0
Implement robust data models, following Architectural designs and solutions that support the organization’s reporting and analytics needs, ensuring high performance and scalability.
Collaborate with business stakeholders to gather data requirements and translate them into technical solutions that drive business insights.
Ensure data accuracy, integrity, and quality by following effective data governance, security, and compliance measures.
Follow version control and continuous integration/deployment (CI/CD) pipelines using Git or other similar tools.
Proactively identify and resolve bottlenecks, optimizing existing data infrastructure to improve efficiency and scalability.
Provide advanced troubleshooting and performance tuning expertise for SQL and data integration solutions.
Maintain Legacy SQL Server Reporting Services (SSRS) while transitioning reporting to modern platforms such as Power BI.