Senior DataOps / Cloud Data Engineer
Assignment Number: A2928568
Client: Government Services Integration Cluster (GSIC), Government of Ontario
Ministry: Ministry of Public and Business Service Delivery and Procurement
Location: 595 Bay Street, Toronto, Ontario
Work Arrangement: 100% Onsite – 5 days per week
Contract Start Date: September 21, 2026
Contract End Date: October 1, 2027
Extension: One possible extension
Openings: 1
Submission Limit: One candidate only
Security Clearance: No clearance required
Position Overview
The Government Services Integration Cluster (GSIC) is seeking an experienced Senior DataOps / Cloud Data Engineer to design, develop, implement, and support enterprise-scale cloud data platforms and integration services. The successful candidate will build scalable data pipelines, optimize data processing, automate metadata management, and enable secure, reliable data exchange across cloud and enterprise environments.
The ideal candidate will bring hands-on expertise in Databricks, Delta Lake, Databricks SQL, Medallion Architecture, Azure Data Services, Azure Data Factory, and Informatica or equivalent integration platforms. Experience operationalizing common data models and metadata repositories is essential.
The resource will collaborate with architects, data modellers, governance teams, and technical stakeholders to deliver cloud data modernization, data quality, lineage, integration, and automation solutions.
Key Responsibilities
Design, develop, implement, and maintain scalable end-to-end data pipelines from source systems to downstream consumers.
Build and optimize ETL/ELT processes for batch and streaming data integration, ingestion, transformation, and provisioning.
Implement cloud-native data engineering solutions using Databricks, Delta Lake, Databricks SQL, and Azure data services.
Apply Medallion Architecture principles to organize, transform, validate, and manage enterprise data.
Develop and maintain integration workflows using Azure Data Factory, Informatica, or equivalent enterprise integration platforms.
Operationalize common data models and metadata repositories, ensuring consistent data definitions, standards, and integration across enterprise systems.
Design and implement automated metadata collection, synchronization, lineage tracking, and data quality processes.
Implement DataOps practices, including CI/CD, automated testing, deployment automation, monitoring, and operational support.
Support cloud data platform implementation, configuration, performance tuning, scalability, and reliability.
Develop and manage data lake, data lakehouse, data warehouse, and cloud storage solutions.
Support large-scale data migration and modernization initiatives involving OLTP and OLAP environments and cloud SaaS, PaaS, and IaaS platforms.
Develop data integration solutions using APIs, event-driven architectures, and batch or streaming technologies.
Implement data governance, security, metadata management, and operational best practices.
Support interoperability and data exchange between cloud platforms, enterprise applications, and on-premises systems.
Collaborate with solution architects, data modellers, governance specialists, and business stakeholders to operationalize enterprise data standards.
Troubleshoot complex data engineering and platform issues and deliver practical, scalable solutions.
Document technical designs, data mappings, workflows, deployment processes, and operational procedures.
Meet project milestones and deadlines while adapting to changing business and technical priorities.
Requirements
Mandatory Skills and Experience
Candidates must demonstrate hands-on experience in the following areas.
1. Databricks, Delta Lake and Databricks SQL
Experience designing, developing, and supporting data engineering solutions using Databricks.
Practical experience with Delta Lake for reliable data storage, transformation, and management.
Experience using Databricks SQL for querying, processing, and working with enterprise data.
Ability to develop and optimize scalable data pipelines within modern cloud data platforms.
2. Medallion Architecture
Knowledge and practical application of Medallion Architecture for organizing data processing and storage.
Understanding of layered data architecture, including Bronze, Silver, and Gold data layers.
Ability to implement data ingestion, cleansing, transformation, validation, and curated data delivery processes.
3. Azure Data Services and Azure Data Factory
Hands-on experience with Microsoft Azure Data Services and Azure Data Factory (ADF).
Experience developing, orchestrating, monitoring, and maintaining data integration pipelines.
Knowledge of cloud data storage, data movement, transformation, scheduling, and workflow automation.
Experience integrating cloud data services with enterprise applications and data platforms.
4. Informatica or Equivalent Integration Platforms
Experience with Informatica or an equivalent enterprise data integration platform.
Ability to develop and maintain integration workflows for data ingestion, transformation, synchronization, and provisioning.
Experience integrating heterogeneous data sources and supporting enterprise data exchange requirements.
5. Common Data Models and Metadata Repositories
Experience operationalizing common data models and metadata repositories within enterprise data environments.
Ability to implement and maintain standardized data definitions, metadata structures, and data mappings.
Experience automating metadata collection, synchronization, and integration with data platforms and governance processes.
Ability to collaborate with data architects, modellers, and governance teams to implement and maintain enterprise data standards.
6. Cloud Data Engineering and DataOps
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 DataOps practices, including CI/CD, deployment automation, automated testing, monitoring, and operational support.
Knowledge of cloud-native data engineering architectures, data security, governance, and operational best practices.
Experience with data platform performance optimization, troubleshooting, and scalability.
7. Data Migration, Integration and Quality
Experience supporting cloud modernization, data migration, and interoperability initiatives.
Knowledge of structured, semi-structured, and unstructured data ingestion, processing, and provisioning.
Experience implementing data quality, metadata, lineage, and data governance capabilities.
Knowledge of API-based integration, event-driven architectures, and batch or streaming data processing.
8. Professional and Communication Skills
Strong analytical, problem-solving, and decision-making abilities.
Excellent written and verbal communication skills.
Strong organizational and interpersonal skills.
Ability to work collaboratively with multidisciplinary technical and business teams.
Demonstrated ability to manage competing priorities, meet tight deadlines, and deliver high-quality solutions.
Desirable Skills and Experience
The following qualifications are considered assets:
Previous experience working with the Ontario Public Service (OPS), other provincial governments, or public-sector organizations.
Experience with Microsoft Purview for metadata management, data cataloguing, lineage, and governance.
Experience with Microsoft Fabric and related Azure data services.
Experience with Databricks Lakehouse solutions and advanced Delta Lake capabilities.
Experience implementing enterprise data lakes, data lakehouses, and data warehouses.
Experience with Informatica or other enterprise integration and data management platforms.
Experience with graph technologies and semantic repositories.
Experience operationalizing enterprise metadata repositories and common data models.
Knowledge of the Accessibility for Ontarians with Disabilities Act (AODA) and related accessibility standards.
Ability to adapt quickly to changing project requirements and delivery priorities.
Key Deliverables
The successful candidate will be responsible for delivering:
Data Pipelines and Integration Services: Scalable, reliable data ingestion, transformation, orchestration, and integration solutions.
Automated Metadata Management: Automated metadata collection, synchronization, and repository integration processes.
Cloud Data Platform Implementation: Configuration and implementation of cloud data services and modern data platform components.
Data Quality and Lineage: Capabilities supporting data validation, quality management, traceability, and lineage.
CI/CD and DataOps Automation: Automated deployment, testing, monitoring, and operational processes.
Data Migration and Interoperability: Services supporting cloud modernization, enterprise data migration, and cross-platform data exchange.
Cloud-Hosted Data Products: Reusable data services, curated datasets, and data products for enterprise consumption.
Metadata and Governance Integrations: Integration of metadata repositories, common data models, and governance platforms into operational data environments.
Ideal Candidate Profile
The ideal candidate is a senior-level cloud data engineering professional with strong hands-on implementation experience across Databricks, Delta Lake, Databricks SQL, Medallion Architecture, Azure Data Factory, and Informatica or equivalent integration technologies. The candidate must also demonstrate practical experience operationalizing common data models and metadata repositories.
Candidates with additional experience in Microsoft Purview, Microsoft Fabric, enterprise data governance, and Ontario public-sector projects will be particularly attractive.