We are seeking a Data Engineering Specialist to conduct work on the full life cycle of data engineering including analysis, solution design, data pipeline engineering, testing, deployment, scheduling, and production support. Reporting to the Director, Data Strategy and Analytics, you will be Accountable for leading and providing data engineering expertise to analytics and modeling through the creation and maintenance of data pipelines, the design and development of data solutions, and performing data management and optimization to support Supply Ontario’s current and future business needs.

Key Responsibilities

• Design and develop scalable, efficient data pipelines using Azure Data Factory and Databricks Workflows.
• Optimize pipeline performance for scalability, throughput, and reliability with minimal latency.
• Implement robust data quality, validation, and cleansing processes to ensure data integrity.
• Collaborate with stakeholders to gather business and technical requirements for data solutions.
• Troubleshoot and resolve data ingestion, transformation, and orchestration issues.
• Support analytics, data science, and machine learning workloads through seamless data integration.
• Support data governance initiatives, ensuring compliance with data security, privacy, and quality standards.
• Contribute to data migration projects including OLTP/OLAP workloads and very large datasets (VLDs) to cloud platforms (SaaS, PaaS, IaaS).
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Required Skills

• +5 years of experience in data engineering, Strong proficiency in Python and familiarity with Azure Services is required.
• Expertise with Azure Data Services: Azure SQL Database, Azure Data Lake, Azure Storage, Azure Databricks.
• Experience with data pipeline development, orchestration, deployment, and automation using ADF, Databricks, Azure DevOps/GitHub Actions.
• Proficiency in Python, Scala, and T-SQL.
• Solid understanding of data warehousing and ETL concepts including star/snowflake schemas, fact/dimension modeling, and OLAP.
• Familiarity with DataOps principles, Agile methodologies, and continuous delivery.
• Proficient in data provisioning automation, data flow control, and platform integration.
• Knowledge of both structured, semi-structured, and unstructured data ingestion, exchange, and transformation.
• Experience with cloud-native data services such as DaaS (Data-as-a-Service), DBaaS (Database-as-a-Service), and DWaaS (Data Warehouse-as-a-Service), and infrastructure elements like Key Vault, VMs, and disks.
• Experience with commercial and open-source data platforms, storage technologies (cloud and on-prem), and the movement of data across environments.
• Experience in performance monitoring and tuning for cloud-based data solutions.
• Experience supporting digital product development, data analysis, data security, and secure data exchange across platforms.
• Proven experience designing enterprise-scale data architectures with high availability and security.
• Understanding of data governance, data security, compliance, and metadata management.
• Proficient in entity-relationship (ER) modeling and dimensional modeling.
• Strong knowledge of normalization/denormalization techniques to support analytics-ready datasets.

MSP Notes

Must Haves:

5+ years of experience in data modelling and data engineering is required

5+ Expertise with Azure Data Services: Azure SQL Database, Azure Synapse Analytics, Azure Data Lake, Azure Storage, Azure Databricks

5+ Experience with data pipeline development, orchestration, deployment, and automation using ADF, Databricks, Azure DevOps/GitHub Actions

Location: Remote

Public Sector Experience: No

# of submissions/supplier: 1