Job Title: Data

Ops/Cloud Data Engineer - Senior Location: Toronto, Ontario, Canada Work Model: Onsite Duration: 12 months Extension: 1 Time Supplier Comments Nice to have: Prior OPS/Public sector exp.

Experience with Microsoft Purview and Microsoft Fabric Must Haves Experience with Databricks, Delta Lake and Databricks SQL.

Knowledge of Medallion Architecture.

Experience with Azure Data Services/Azure Data Factory Experience with Informatica or equivalent integration platforms.

Experience operationalizing common data models and metadata repositories.

Description / Responsibilities / Skills Responsibilities Designing and developing data pipelines from source to end user Optimizing data pipelines General Skills Experience in DataOPS principle, best practice, and implementation and Agile project development and deployment Experience in Continuous Integration/Continuous Development/Deployment (CI/CD) and Data provisioning Automation Experience with DataOPS performance monitoring and tuning A team player with a track record for meeting deadlines Experience & Skill Set Requirements General Skills (80%) Demonstrated experience designing, implementing and supporting cloud-based data platforms and services.

Experience developing scalable ETL/ELT processes, data pipelines and integration solutions.

Experience implementing Data

Ops practices including deployment automation, testing, monitoring and operational support.

Experience with cloud-native data engineering and up-to-date data platform architectures.





Experience implementing metadata, lineage and data quality capabilities within cloud data environments.

Experience developing integration solutions using APIs, event-driven architectures and batch or streaming technologies.

Knowledge of data governance, security and operational best practices for cloud platforms.

Experience automating metadata collection, data movement and operational processes.

Experience supporting cloud modernization and data migration initiatives.

Knowledge of performance optimization and scalability considerations for cloud data platforms.

Ability to troubleshoot complex technical problems and develop practical solutions.

Strong analytical, organizational and communication skills.

Resource will support the following deliverables: Data pipelines and integration services Automated metadata collection and synchronization processes Cloud data platform implementation and configuration Data quality and lineage enablement CI/CD and Data

Ops automation Data migration and interoperability services Cloud-hosted data products and reusable data services Metadata and governance platform integrations Desirable Skills (20%) Experience with Microsoft Fabric and Azure Data Services.

Experience with Microsoft Purview.

Experience with graph technologies and semantic repositories.

Knowledge and understanding of AODA and related standards.

Ability to meet tight deadlines and adapt to changing project priorities.

📌 DataOps/Cloud Data Engineer – Senior (Toronto)
🏢 Innosystech
📍 Toronto