Client: Ministry of Public and Business Service Delivery and Procurement

Work Location: 777 Bay St., Toronto, Ontario – Onsite Estimated Start Date: September 1, 2026

Estimated End Date: September 1, 2027

Business Days: 250

Extension: Probable after the initial mandate

Hours: 7.25 hours per day

Security Level: No Clearance Required

Application Deadline: Friday, August 28, 2026 at 10:00 AM EST

Role Overview

We are seeking a Senior DataOps/Cloud Data Engineer to design, develop, optimize, and support enterprise-scale cloud data solutions and pipelines.

The successful candidate will have strong hands-on experience with Informatica, Azure Data Factory, Databricks, Python, SQL, T-SQL, PL/SQL, SSIS, and Microsoft Fabric , with demonstrated expertise in data pipeline development, orchestration, automation, cloud data platforms, and Databricks Medallion Architecture .

This role will focus on building and optimizing data pipelines from Oracle and other on-premises sources into cloud Lakehouse environments, developing scalable data models, supporting large-scale data migrations, implementing DataOps and data quality practices, and ensuring secure, governed, and traceable data movement across enterprise platforms.

Must-Have Skills

- Experience developing ETL processes using Informatica, Azure Data Factory, and Databricks.
- Robust experience with Python, SQL, Lakeflow, and SQL optimization.
- Experience developing solutions using Databricks Medallion Architecture.
- Strong experience with cloud data engineering and multiple programming/data technologies, including:

- Python

- SQL
- T-SQL

- PL/SQL

- Informatica

- Azure Data Factory

- SSIS

- Microsoft Fabric
- Experience developing, orchestrating, deploying, and automating data pipelines and workflows.
- Experience managing dataflow, Delta Lake indexing, parallelism, and data movement.
- Experience designing and optimizing Azure Data Factory and Databricks pipelines from Oracle databases to Lakehouse environments.
- Experience translating Informatica ETL to Azure Data Factory and Databricks ELT.

Key Responsibilities
- Cloud Data Engineering & Pipeline Development
- Design, develop, and optimize Azure Data Factory and Databricks pipelines.
- Build data pipelines from Oracle and other on-premises databases into cloud Lakehouse environments.
- Develop and manage data processing workflows, orchestration, deployment, and automation.
- Develop scalable pipelines to manage dataflow, Delta Lake indexing, parallelism, and data movement.
- Build automated data jobs, scripts, and integration processes across multiple platforms.
- Develop and optimize data connections between Databricks Medallion Architecture and on-premises data sources.
- Support downstream data consumers with reliable and optimized data services.
- Databricks & Medallion Architecture
- Design and develop enterprise data solutions using Databricks Medallion Architecture.
- Develop and optimize Bronze, Silver, and Gold data layers.
- Design and optimize data models supporting Lakehouse environments.
- Manage Delta Lake storage, indexing, processing, and performance.
- Support data Lakehouse architecture and implementation.
- Develop solutions using Databricks and/or Microsoft Fabric.
- Apply cloud data engineering best practices to scalable enterprise environments.
- ETL/ELT Modernization & Data Migration
- Translate existing Informatica ETL processes into Azure Data Factory and Databricks ELT solutions.
- Design and implement data conversion and migration strategies for very large datasets.
- Support migration of OLAP and OLTP environments to cloud SaaS, PaaS, and IaaS environments.
- Develop data mapping and transformation processes.
- Support enterprise data warehouse, data lake, and data Lakehouse implementations.
- Design fact and dimension models and enterprise data structures.
- Develop star-schema and multi-dimensional data models.
- Document detailed logical and physical data models.
- Data Architecture, Modelling & Integration
- Design and optimize relational data models.
- Develop conceptual, logical, and physical data models.
- Design data warehouses, data lakes, and Lakehouse solutions.
- Support structured, semi-structured,



and unstructured data ingestion and provisioning.
- Develop data exchange and integration solutions.
- Integrate APIs and data exchange technologies.
- Support digital product data requirements and data provisioning.
- Apply information architecture and enterprise data standards.
- DataOps, Quality & Performance
- Implement DataOps best practices across cloud data engineering processes.
- Monitor and tune DataOps and pipeline performance.
- Implement data quality checks and validation processes.
- Perform data profiling, cleansing, validation, and monitoring.
- Identify and resolve data quality issues.
- Develop and maintain data lineage reports to provide end-to-end visibility into data movement and transformations.
- Implement continuous integration, continuous development, and continuous deployment (CI/CD) practices.
- Automate data provisioning and deployment processes.
- Data Security, Governance & Compliance
- Apply data governance practices across enterprise data environments.
- Integrate and manage Microsoft Entra ID for authentication, authorization, and role-based access control.
- Implement data anonymization and masking techniques.
- Protect sensitive and regulated data in accordance with privacy requirements.
- Apply security principles across cloud data platforms and data pipelines.
- Ensure solutions comply with applicable AODA/WCAG requirements.

- Cloud Data Services
- Manage cloud data services supporting project delivery, including:

- Azure Storage Accounts

- Data Lakehouse

- Key Vault

- Virtual Machines

- Parquet files

- Data repositories

- Cloud storage platforms
- Support cloud and on-premises data integration.
- Work with data storage, database, and data exchange technologies.
- Support Data as a Service (DaaS), Database as a Service (DBaaS), and other cloud storage platforms.
- Production Support & Troubleshooting
- Troubleshoot production data pipeline and application issues.
- Investigate defects and implement corrective fixes.
- Support unplanned deployments and production fixes.
- Perform defect investigation, resolution, and assignment.
- Monitor system performance and resolve data processing issues.
- Participate in system, integration, and user acceptance testing.
- Support SIT, SAT, UAT, and unit testing activities.

Experience & Skill Set Requirements Technical Experience – 40%

- Strong experience developing ETL processes using Informatica, Azure Data Factory, and Databricks.
- Strong experience with Python, SQL, T-SQL, PL/SQL, Informatica, ADF, SSIS, and/or Microsoft Fabric.
- Experience developing solutions using Databricks Medallion Architecture.
- Experience with cloud data platforms, data management, and data exchange technologies.
- Experience managing Delta Lake and cloud data storage environments.
- Experience developing and managing enterprise data pipelines and workflows.
- Experience with data orchestration, deployment, automation, and pipeline optimization.
- Experience with CI/CD and data provisioning automation.
- Experience with data integration APIs and data exchange.
- Experience with very large-scale OLAP and OLTP data migration to cloud environments.
- Experience designing fact/dimension models, data warehouses, data lakes, and Lakehouse solutions.
- Experience developing star-schema and multi-dimensional models.
- Experience managing Azure cloud data services.
- Experience with structured, semi-structured, and unstructured data.
- Experience with DataOps performance monitoring and tuning.
- Experience implementing data quality, profiling, cleansing, validation, and monitoring.
- Experience applying data governance and information architecture standards.
- Experience integrating Microsoft Entra ID and implementing role-based access control.
- Experience implementing data anonymization and masking.
- Experience developing data lineage reports.




- Databricks and/or Microsoft Fabric certification is an asset.

Core Skills – 35%
- Excellent analytical, problem-solving, decision-making, communication, presentation, interpersonal, and negotiation skills.
- Strong solution development and design experience across technology, data, databases, applications, statistics, and networking.
- Experience leading or supporting Business Intelligence and enterprise data projects.
- Strong understanding of DataOps best practices and Agile development/deployment.
- Experience analyzing large and diverse datasets.
- Experience with data visualization, KPI development, and data profiling.
- Experience manipulating and analyzing complex, high-volume structured and unstructured data.
- Experience preparing business, system, and technical requirements documentation.
- Experience with advanced data visualization tools and techniques.
- Experience troubleshooting production issues and implementing corrective fixes.
- Experience with defect investigation and resolution.
- Experience coordinating modernization of large, complex, multi-platform, multi-tier IT systems.
- Experience with Azure DevOps.
- Experience with AODA/WCAG compliance.
- Strong track record of meeting deadlines and delivering high-quality solutions.

Project Experience & Techniques – 15%
- Experience with SDLC processes and Agile and Waterfall methodologies.

- Experience conducting

- Unit Testing

- System Testing

- SIT

- SAT

- UAT
- Experience communicating daily project tasks, issues, risks, and progress.
- Experience tracking and facilitating resolution of technical issues and risks.
- Experience gathering and consolidating business and system requirements.
- Experience managing change requests and project artifacts.
- Experience leading requirements-gathering sessions.

General Skills – 5%
- Strong problem-solving and decision-making abilities.
- Excellent written and verbal communication skills.
- Experience working with and leading multiple teams.
- Ability to coordinate multiple projects with competing priorities.
- Proven ability to meet strict deadlines.
- Strong collaboration and team-integration skills.
- Experience providing technical advice and guidance.
- Ability to work effectively with business users and IT development teams.
- Ability to ensure business requirements are accurately reflected in system designs and technical specifications.

Organization Experience – 5%
- Previous public sector experience within an organization of comparable size and complexity.
- Experience working within large, complex, enterprise technology environments.

Key Deliverables & Outcomes
- Design and deliver scalable Azure and Databricks data pipelines.
- Modernize legacy Informatica ETL processes into ADF and Databricks ELT solutions.
- Build and optimize enterprise Lakehouse solutions using Medallion Architecture.
- Deliver reliable and efficient data movement between on-premises and cloud environments.
- Develop enterprise data models, data warehouses, data lakes, and Lakehouse solutions.
- Implement strong DataOps, CI/CD, data quality, security, governance, and lineage practices.
- Support large-scale cloud data migration and modernization initiatives.
- Ensure secure, governed, traceable, and high-quality enterprise data.
- Provide production support, troubleshooting, technical documentation, and knowledge transfer.
- Collaborate effectively with business and technical stakeholders to deliver project outcomes.

How to Apply If you are interested and your profile matches the requirements, please send the following documents to [email protected] by Friday, August 28, 2026 at 10:00 AM EST :

- Updated Resume in Word format – Mandatory
- References – Mandatory
- Expected Hourly Rate – Mandatory
- Visa Status – Mandatory
- LinkedIn ID – Mandatory

Important: Applications without all mandatory documents cannot be processed. If this opportunity is not suitable for you, please feel free to forward it to qualified Senior DataOps/Cloud Data Engineers with strong Informatica, Azure Data Factory, Databricks, Python, SQL, Medallion Architecture, ETL/ELT, DataOps, cloud migration, data modelling, and enterprise data engineering experience.

📌 – DataOps/Cloud Data Engineer – Senior (Toronto)
🏢 S M Software Solutions
📍 Toronto