DataOps/Cloud Data Engineer

Toronto, ON
On-site
Posted Oct 21, 2026
1 position

$80–$85 / hour

Opens LinkedIn

Employment type: Contract
Experience level: Lead · 10+ years
Posting language: English
Working hours: 40 hours per week
Seniority: Mid-Senior level
Application method: Direct apply is available

Job summary

Design, develop, optimize, and support cloud-based data pipelines, integration services, and data platforms. Deliver metadata automation, data quality and lineage capabilities, CI/CD and DataOps automation, and cloud data migration and interoperability services.

Job details

Russell Tobin & Associates is hiring on behalf of one of its esteemed clients. Job Title: DataOps/Cloud Data Engineer Location: Toronto, ON, Canada (On-site) Duration: 283 business days (Possible extension) Client: Public Sector Pay Rate: $80 - $85/hr [CAD] Description: Responsibilities • Designing and developing data pipelines from source to end user • Optimizing data pipelines Must Have: 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. General Skills • Experience with Cloud data platforms, data management and data exchange tools and technologies • Experience with Commercial and opensource data and database development and management and specializing in data storage setting and managing cloud Data As a Service (DaaS). application Database as a Service (DBaaS), Data Warehouses as a Service (DWaaS), and other storage platforms (both in the cloud and on-premise). • Experience with Data pipeline and workflow development, orchestration, deployment, and automation and specializing in programming and pipelines to create and managing the dataflow and movement of data. • Experience with Cloud data engineer must be familiar and experienced with different programming languages and be able to integrate with many different platforms to create data pipelines, automate tasks, and write scripts. • 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 digital product, data analysis, data exchange, data provisioning, and data security • Extensive expert experience in designing/developing and implementing data conversation and migration of VLD (Very large Data) of Online analytical processing (OLAP) and online transaction processing (OLTP) environments to Cloud Software-as-a-service (SaaS), Platform-as-a-service (PaaS) and Infrastructure-as-a-service (IaaS) environments. • Experience in design, development and implementation of fact/dimension model, data mapping, data warehouse, data lake and data lakehouse for enterprise • Experience managing Cloud Data services for project delivery, including storage, repositories, Data Lake, Data Lakehouse, key vault, virtual machine, disk, etc. • Experience with structured, semi-structured, unstructured data collection, ingestion, provisioning and exchange technological development of enterprise data warehouse and data lake and data lakehouse solutions and operational support. • Experience with DataOPS performance monitoring and tuning • Excellent analytical, problem-solving and decision-making skills; verbal and written communication skills; presentation skills; interpersonal and negotiation skills • A team player with a track record for meeting deadlines Experience and 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 DataOps practices including deployment automation, testing, monitoring and operational support. Experience with cloud-native data engineering and modern 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. Experience collaborating with architects, modellers and governance teams to operationalize data standards, metadata and common data models. 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 DataOps automation Data migration and interoperability services Cloud-hosted data products and reusable data services Metadata and governance platform integrations Desirable Skills (20%) Experience with Databricks, Delta Lake and Databricks SQL. Knowledge of Medallion Architecture. Experience with Microsoft Fabric and Azure Data Services. Experience with Microsoft Purview. Experience with Informatica or equivalent integration platforms. Experience operationalizing common data models and metadata repositories. 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. Nice to have: Prior OPS/Public sector exp. Experience with Microsoft Purview and Microsoft Fabric If this opportunity matches your experience and career goals, please share your most updated resume for consideration.

What you’ll do

Requirements

The role requires strong experience designing and supporting cloud data platforms, scalable ETL/ELT pipelines, integration solutions, and DataOps practices. Candidates should be skilled in metadata, lineage, data quality, governance, cloud migration, and performance optimization; Databricks, Azure services, Microsoft Fabric or Purview, and Informatica are listed as desirable skills.