DataOps/Cloud Data Engineer (RQ11576)

Softline Technology Inc.; Toronto; On-site; CAD 90,000 - 130,000; Full time

Job summary

Softline Technology Inc. in Toronto is seeking a Data Engineer to design and develop data pipelines, optimize data flows, and enable end-user access to data assets on cloud platforms.

The role focuses on building scalable ETL/ELT processes, implementing DataOps practices, and integrating across Databricks, Delta Lake, and Azure data services to support BI and analytics. On-site five days a week, you will collaborate with architects and governance teams to ensure data quality, security, and

Qualifications

• Demonstrated experience designing and supporting cloud-based data platforms.
• Experience developing scalable ETL/ELT processes and data pipelines.
• Experience implementing DataOps with deployment automation and monitoring.
• Knowledge of metadata, lineage and data quality in cloud environments.
• Ability to integrate APIs and event-driven architectures.
• Strong analytical and communication skills; able to meet deadlines.

Responsibilities

• Design, develop and optimize data pipelines from source to end user.
• Manage data platform implementations and cloud storage deployments.
• Implement CI/CD and DataOps automation.
• Support data migration and interoperability across services.

Skills

Data Engineering

Cloud data platforms

ETL/ELT pipelines

CI/CD and DataOps

APIs and integrations

Data governance

SQL/NoSQL

Tools

Databricks

Delta Lake

Databricks SQL

Microsoft Purview

Azure Data Services

Informatica

Medallion Architecture

Job description

Responsibilities

• Designing and developing data pipelines from source to end user
• Optimizing data pipelines

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 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 enablementCI/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.

Supplier Comments

Closing Date/Time: 2026-10-26, 12:00 p.m. EST.

Max submission: 1 (one)

5 days onsite

HM is looking for a Data Engineer – with the tech stack below:

Must Have:

• Experience with Databricks, Delta Lake and Databricks SQL.
• Knowledge of Medallion Architecture.
• Experience with Azure Data Services/Azure Data Factor y
• Experience with Informatica or equivalent integration platforms.
• Experience operationalizing common data models and metadata repositories.

Nice to have:

• Prior OPS/Public sector exp.
• Experience with Microsoft Purview and Microsoft Fabric