Senior QA Automation Engineer

Position: Senior QA Automation Engineer Duration: 12-Month Contract (with high potential for extension or permanent conversion) Location & Work Arrangement: Toronto, ON (Hybrid: 4 days per week on-site at the corporate office) Advantages: - High-Impact Financial Platform Scope: Direct quality engineering and automated controls for mission-critical enterprise treasury, liquidity, and regulatory data systems. - Competitive Hourly Rate: Attractive compensation ranging from $115/hr to $130/hr based on candidate technical depth. - Modern Cloud & AI Sandbox: Build automated test suites across modern Azure Data Lakehouse architectures while utilizing AI-enabled tools (GitHub Copilot, agentic workflows) to accelerate delivery. - Substantial Contract Duration: Secure a full 12-month consulting engagement within a well-funded, stable financial technology group. Responsibilities: - Automation Framework Engineering: Design, build, and maintain automated testing frameworks for cloud data pipelines, REST APIs, and application workflows using Python, Selenium, PyTest, and custom automation utilities. - Large-Volume Data Validation: Test complex data pipelines by building automated quality controls for reconciliation, data completeness, schema validation, accuracy checks, and anomaly detection. - Cloud Data Platform Testing: Develop and execute test strategies for Azure-based cloud data environments, including Azure Databricks, Spark SQL, Delta Lake, PySpark, ADLS, and Azure Data Factory orchestration. - API & Integration Testing: Build automated validation suites for REST APIs, data ingestion endpoints, and downstream analytics reporting outputs. - AI-Enabled Efficiency: Apply Microsoft Copilot, GitHub Copilot, and agent-based workflows to accelerate test design, test case generation, defect analysis, and documentation while maintaining strict engineering review practices. - Cross-Functional Collaboration & Compliance: Partner with business users, BSAs, developers, and platform leads to translate business requirements into audit-ready validation artifacts, defect summaries, and control evidence. Qualifications: - Technology & QA Experience: 6 to 10+ years of overall software technology experience, including hands-on QA automation, test framework development, and testing complex data or application platforms. - Scripting & Automation Depth: Strong programming proficiency in Python (or Perl/similar languages), with hands-on experience using Selenium, PyTest, and modern API testing frameworks. - SQL & Data Testing Mastery: Advanced SQL skills and proven data validation experience testing large-volume data movement, source-to-target reconciliation, completeness, schema checks, and data quality rules. - Azure Cloud Data Ecosystem: Practical experience with Azure data platforms, including Azure Databricks, Spark SQL, Delta Lake, PySpark, Azure Data Lake Storage (ADLS), and Azure Data Factory (ADF). - AI Tooling Proficiency: Demonstrated ability to effectively utilize AI engineering tools (GitHub Copilot, Microsoft Copilot, agent-based workflows) to accelerate test automation, code design, and defect profiling. - Communication & Collaboration: Strong interpersonal skills with the ability to articulate technical risk, present test coverage, and collaborate effectively with both technical and non-technical partners. Preferred Assets & Nice-to-Haves: - Experience with Databricks notebooks, PySpark-based validation, Delta Lake quality checks, medallion/lakehouse architecture testing, and Spark SQL performance optimization. - Familiarity with data quality, observability, or CI/CD pipeline tools (e.g., Great Expectations, Deequ, dbt testing, Airflow, Azure DevOps, Jenkins). - Domain experience within Financial Services, Treasury, Capital Markets, Liquidity, Regulatory Reporting, or Balance Sheet Management. - Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical discipline.