Purpose of the Job

We are seeking a highly skilled and experienced Senior Quality Engineer to join our Enterprise Technology and Data team. This role is crucial in ensuring the delivery of high-quality data-driven solutions and applications across our enterprise systems. The ideal candidate will have deep expertise in quality engineering, automated testing, and extensive experience in testing large-scale data systems, particularly in ETL processes and working with Azure Fabric and Google Cloud data platforms such as BigQuery and Google Cloud Data Pipelines.

As a Senior Quality Engineer, you will be responsible for defining and implementing quality assurance processes, developing automated tests, identifying and mitigating risks, and ensuring the overall quality of our technology and data platforms across multi-cloud environments (Azure and Google Cloud Platform). You will work closely with cross-functional teams including developers, data engineers, and product managers to ensure that the highest standards of quality are maintained throughout the software development lifecycle. Main Activities:

Test Planning & Execution: Design detailed test strategies/plans, test cases, and test scripts to validate data accuracy, integrity, and system functionality in ETL pipelines across Azure and GCP (e.g., Dataflow, Data Fusion, BigQuery pipelines), including both functional and non-functional testing.

Data Quality: Collaborate with data engineers to monitor and ensure the accuracy, completeness, and consistency of data across various data platforms including Azure Synapse/Fabric and Google BigQuery, validating transformations and data integrity in ETL pipelines.

Data Pipeline Testing: Collaborate with data engineering teams to design and execute tests for Data pipelines running on Azure Fabric as well as Google Cloud services such as Dataflow, Dataproc, and Pub/Sub-based pipelines, ensuring transformation, integration, and storage processes function as expected.

SQL Database Testing: SQL Database Testing: Development and execution of SQL-based automated tests for database validation, ensuring data integrity, performance,



and security across multiple databases including BigQuery datasets and Azure SQL/Synapse environments.

Performance Testing: Conduct performance testing to evaluate the scalability, stability, and performance of large-scale data systems across distributed cloud platforms including Azure and GCP.

Automated Testing: Develop and maintain automated test frameworks for data applications, including ETL processes, APIs, and data integration systems using industry-standard tools, and extend automation to support BigQuery validation, GCP pipeline testing, and AI/ML data validation scenarios.

Defect Tracking & Resolution: Work with development and operations teams to identify, report, and track defects across multi-cloud data ecosystems, ensuring timely resolution.

Continuous Improvement: Propose and implement improvements to testing processes and frameworks to increase efficiency and effectiveness of quality assurance efforts across Azure and Google Cloud-based data platforms and AI-enabled data solutions.

Collaboration: Partner with business stakeholders, project managers, and technical teams to ensure alignment with quality goals and deliverables across enterprise data platforms spanning multiple cloud providers.

Mentorship: Provide guidance and mentorship to junior quality engineers, helping them improve their technical skills in modern data testing practices including cloud-native and AI-driven validation techniques.

Compliance & Best Practices: Ensure adherence to industry standards and best practices related to software quality, particularly in relation to data systems, ETL processes, and cloud technologies including Azure and Google Cloud Platform environments. Knowledge/Skill Requirements:

Education

Bachelor’s degree in Computer Science,



Information Technology, Engineering, or a related field. Experience:

5+ years of experience in software quality engineering, with at least 3 years of experience in an enterprise data environment.

Proven experience in automated testing tools (e.g., Selenium, JUnit, TestNG, or similar frameworks).

Hands-on experience with data validation, transformation, and data pipeline testing in cloud environments, specifically Azure and/or Google Cloud Platform (GCP).

Strong understanding of data management, data warehousing, ETL processes, and data integration.

Experience with data warehousing, data lakes, and cloud-based ETL tools including Azure Data Factory, Azure Synapse, Google BigQuery, Dataflow, and Dataproc is a plus.

Technical Skills

Proficient in scripting languages (e.g., Python, Java, SQL) for test automation and ETL process validation.

Experience working with Azure Data Factory, Azure Synapse Analytics, Microsoft Fabric, as well as Google Cloud services such as BigQuery, Dataflow and Cloud Composer (Airflow) for data pipeline orchestration and validation.

Strong knowledge of software development methodologies (Agile, Scrum, Waterfall) and experience in a continuous integration/continuous deployment (CI/CD) environment.

Familiarity with version control systems (e.g., Git), CI/CD tools (e.g., Jenkins, Azure DevOps), and cloud infrastructure (e.g., Azure, GCP).

Soft Skills

Strong analytical, problem-solving, and troubleshooting skills.

Excellent communication skills, with the ability to collaborate effectively across departments.

Detail-oriented with a passion for ensuring high-quality standards in data-driven solutions.

Ability to work independently and as part of a team, managing multiple priorities in a quick-paced environment.

Certifications/Additional Training – Nice-to-Have:

Relevant certifications (e.g., Azure DevOps, Azure ML, ISTQB, Google Cloud Professional Data Engineer or Professional Cloud Developer) are a plus.

Experience working with data governance tools such as Azure Purview and ensuring data compliance.

📌 Data Quality Engineer (Toronto)
🏢 TAO Digital Solutions
📍 Toronto