DataOps/Cloud Data Engineer

About ProxyLink

At ProxyLink, we specialize in building meaningful connections between exceptional talent and leading employers across Canada. As a trusted recruitment partner, our mission is to create impactful career matches by combining innovative hiring strategies with a personalized, people-first approach. We’re driven by the belief that investing in people today builds a stronger workforce for tomorrow.

ProxyLink is working with a client hiring for an experienced Data Engineer to design, develop, implement, and optimize cloud-based data platforms, data pipelines, and integration solutions.

The ideal candidate will have strong hands-on experience with modern data engineering technologies, cloud data services, ETL/ELT processes, data migration, and DataOps practices.

This role will focus on building scalable and secure data solutions, automating data workflows, improving data quality, and enabling efficient data exchange across enterprise platforms.

Work Arrangement: Onsite, five days per week, Monday to Friday.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines from source systems to end users.
• Optimize data pipelines for performance, reliability, scalability, and cost efficiency.
• Develop and support cloud-based data platforms, integration services, and reusable data solutions.
• Implement ETL/ELT processes, workflow orchestration, deployment automation, and data provisioning.
• Support cloud data platform implementation, configuration, and operational maintenance.
• Design and implement data warehouses, data lakes, and data lakehouses.
• Develop and maintain data models, including fact and dimension models, data mappings, and common data models.
• Support the collection, ingestion, transformation, synchronization, and exchange of structured, semi-structured, and unstructured data.
• Implement automated metadata collection, synchronization, data lineage, and data quality processes.
• Apply DataOps principles and practices to improve development, deployment, testing, monitoring, and operational support.
• Develop and maintain CI/CD pipelines and automated data provisioning processes.
• Support large-scale data migration and modernization initiatives involving OLTP and OLAP environments and cloud-based platforms.
• Develop integration solutions using APIs, event-driven architectures, batch processing, and streaming technologies.
• Implement data governance, security, metadata management, and operational best practices.
• Collaborate with architects, data modelers, and governance teams to implement data standards and enterprise data models.
• Monitor, troubleshoot, and resolve complex technical issues affecting data pipelines and cloud data services.
• Identify opportunities to automate data movement, metadata management, and operational workflows.

Required Skills and Experience

1. Cloud Data Engineering & Platform Development

• Demonstrated experience designing, implementing, and supporting cloud-based data platforms and services.
• Experience developing scalable ETL/ELT processes, data pipelines, and data integration solutions.
• Strong understanding of cloud-native data engineering and modern data platform architectures.
• Experience managing cloud data services, including storage, repositories, data lakes, data lakehouses, key vaults, virtual machines, and disks.
• Experience supporting cloud modernization and data migration initiatives.
• Knowledge of data storage, cloud data services, and enterprise data management technologies.

2. DataOps, Automation & CI/CD

• Experience implementing DataOps practices, including automated deployment, testing, monitoring, and operational support.
• Hands-on experience with Continuous Integration and Continuous Delivery/Deployment (CI/CD).
• Experience automating data provisioning, metadata collection, data movement, and workflow orchestration.
• Knowledge of Agile development and deployment methodologies.
• Experience with DataOps performance monitoring, troubleshooting, and tuning.

3. Data Architecture, Modeling & Integration

• Experience designing and implementing enterprise data warehouses, data lakes, and data lakehouses.
• Knowledge of fact and dimension modeling, data mapping, and common data models.
• Experience integrating structured, semi-structured, and unstructured data sources.
• Experience with APIs, event-driven architectures, batch processing, and streaming technologies.
• Knowledge of metadata management, data lineage, data quality, and data governance.
• Experience operationalizing metadata repositories and enterprise data standards.
• Understanding of data interoperability, data exchange, and reusable data services.

4. Data Migration, Security & Performance

• Experience supporting large-scale data migration and conversion initiatives across OLTP and OLAP environments.
• Knowledge of cloud service models, including SaaS, PaaS, and IaaS.
• Experience implementing data security, governance, and access management practices.
• Strong understanding of cloud data platform performance optimization, scalability, and reliability.
• Ability to troubleshoot complex technical issues and develop practical, sustainable solutions.

5. Communication & Collaboration

• Excellent analytical, problem-solving, and decision-making skills.
• Strong written and verbal communication and presentation skills.
• Effective interpersonal, collaboration, and negotiation skills.
• Ability to work effectively with cross-functional technical teams.
• Strong organizational skills and a demonstrated ability to meet deadlines and adapt to changing priorities.

Technical Environment

Candidates should have experience with the following technologies and capabilities:

• Databricks
• Delta Lake
• Databricks SQL
• Microsoft Azure Data Services
• Azure Data Factory
• Informatica or equivalent data integration platforms
• Common data models and metadata repositories
• Cloud data warehouses, data lakes, and data lakehouses
• CI/CD and DataOps automation

Must-Have Qualifications

• Hands-on experience with Databricks, Delta Lake, and Databricks SQL.
• Knowledge of Medallion Architecture.
• Experience with Azure Data Services and Azure Data Factory.
• Experience with Informatica or equivalent integration platforms.
• Experience operationalizing common data models and metadata repositories.

Expected Deliverables

• Developed and optimized data pipelines and integration services.
• Automated metadata collection and synchronization processes.
• Implemented and configured cloud data platforms.
• Enabled data quality, metadata management, and data lineage capabilities.
• Implemented CI/CD and DataOps automation.
• Delivered data migration and interoperability solutions.
• Developed cloud-hosted data products and reusable data services.
• Integrated metadata and data governance platforms.