Data Engineer building data integration pipelines for data lakes and warehouses. Collaborating with stakeholders to meet business requirements in a leading publishing company.
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Azure & Databricks Data Engineers (MP4)
Posted via LinkedIn Recruiter (not a company profile)
Posted last month
About the role
- Azure & Databricks Data Engineer role designing and building data pipelines using Microsoft tech stack. 11-month contract, hybrid work in Oshawa, $90-95/hr.
Responsibilities
- As an Azure and Databricks Data Engineer, the role focuses on designing, building, and supporting data-driven applications that enable innovative, customer-centric digital experiences. Work as part of a cross-discipline agile team, collaborating to solve problems across business areas. Build reliable, supportable, and performant data lake and data warehouse products to support reporting, analytics, applications, and innovation. Apply best practices in development, security, accessibility, and design to deliver high-quality services. Develop modular and scalable ELT/ETL pipelines and data infrastructure leveraging diverse enterprise data sources. Create curated common data models in collaboration with Data Modelers and Data Architects to support business intelligence, reporting, and downstream systems. Clean, prepare, and optimize datasets with strong lineage and quality controls throughout the integration cycle. Support BI Analysts with dimensional modeling and aggregation optimization for visualization and reporting. Collaborate with Business Analysts, Data Scientists, Senior Data Engineers, Data Analysts, Solution Architects, and Data Modelers.
Requirements
- Completion of a four-year university program in computer science, engineering, or related data disciplines. Experience designing and building data pipelines, with strong Python, PySpark, SparkSQL, and SQL skills. Experience with Azure Data Factory, ADLS, Synapse, and Databricks, and building pipelines for Data Lakehouses and Warehouses. Strong understanding of data structures, governance, and data quality principles, with effective communication skills for technical and non-technical audiences.
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