Curinos operates under a hybrid modality and has office locations in New York, Chicago, Boston, Toronto, and London. This role is open to remote candidates based in Canada and able to travel as needed.
Job DescriptionCurinos is building a
Data Validation Suite that ensures the accuracy, consistency, and reliability of our data APIs and backend data pipelines. We are looking for a
QA Automation Engineer with strong API and UI automation skills, data testing aptitude, and a proven ability to
leverage AI-powered tools across all QA activities. You do
not need to be a full Data Engineer - we will train you in our Databricks workflows and ETL validation processes.
Responsibilities1. API & UI Automation- Build, maintain, and execute automated API validation suites using Postman/Newman and Python across multiple environments and datasets
- Design, develop, and maintain UI automation frameworks using Selenium or Playwright to validate data-driven web applications, dashboards, and reports
- Develop end-to-end automated test suites validating full data flow from backend data pipelines and APIs to front-end UI visualizations
- Ensure data rendered in UI components accurately matches underlying data sources and business logic
- Contribute to scalable and reusable automation frameworks with configuration-driven API coverage and maintainable Page Object Models
- Leverage AI coding assistants (e.g., GitHub Copilot, Claude) to accelerate test development and auto-generate test cases from OpenAPI/Swagger specifications and UI requirements
- Apply AI-powered visual testing and self-healing locator capabilities to detect UI regressions and reduce test maintenance effort
2. Data Validation & Quality Assurance- Design and execute comprehensive data quality validation across platforms, including source-to-target mappings, transformation logic, and business rule enforcement
- Perform SQL-based data analysis in Databricks to validate completeness, accuracy, null handling, referential integrity, duplication, and aggregation correctness
- Validate schema evolution, data freshness, and cross-system reconciliation across upstream sources, data lake/warehouse, and downstream APIs
- Develop and maintain Python-based data quality frameworks, validation libraries, and automation pipelines
- Capture, version, and compare API responses across test runs using JSON baseline comparisons
- Ensure adherence to data governance, audit, and control requirements, with strong focus on financial and regulated datasets
- Use AI/ML-driven data profiling to detect anomalies, distribution shifts, and pattern deviations
3. Reporting & Root Cause Analysis- Identify, analyze, and report data quality and automation failures, performing root cause analysis in collaboration with engineering and data teams
- Build and maintain quality dashboards using Power BI, Databricks SQL, or Python to track historical trends and validation outcomes
- Define, monitor, and report data quality KPIs including accuracy, completeness, timeliness, consistency, and validity
- Use AI-based summarization tools to auto-generate execution reports, defect summaries, and quality trend insights, including predictive indicators where applicable
4. Collaboration & Agile Practices- Collaborate closely with Data Scientists, Engineers, Product Managers, and stakeholders to define data requirements and quality expectations
- Participate actively in Agile ceremonies and contribute to sprint planning, execution, and retrospectives
- Document test strategies, automation coverage, validation workflows, and API contracts in Confluence
- Leverage AI assistants for test planning, coverage gap analysis, risk-based prioritization, and consistent documentation maintenance
Salary Range: 100,000-110,000 CAD (plus Bonus)
Desired Skills & ExpertiseQA & Automation- 4-7 years of experience in QA Automation
- Strong hands-on experience with Postman/Newman (API) and at least one UI framework: Selenium, Robot, Playwright or Cypress
- Solid Python scripting skills for automation, JSON handling, and building data quality validation scripts
- Experience with CI/CD integration (Jenkins, GitHub Actions) for both API and UI test pipelines
- Familiarity with page object models, cross-browser testing, JSON diff libraries, and data-driven test approaches
Data Validation & SQL- Proficient SQL skills (joins, subqueries, window functions, CTEs, aggregations) for data analysis and validation
- Experience validating source-to-target mappings, ETL outputs, transformation logic, and business rule compliance
- Understanding of data quality dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness
- Ability to perform cross-system data reconciliation (source vs. target, API vs. database) and schema validation
- Familiarity with data governance, audit requirements, and compliance standards for financial data
- Experience with a cloud data platform (Databricks, Snowflake, Redshift, BigQuery) is a plus
AI Tools & Productivity- Experience using AI coding assistants (GitHub Copilot, Cursor, Codeium) for writing and refactoring test code
- Hands-on familiarity with LLM-based tools (ChatGPT, Claude, Gemini) for test case generation, debugging, data analysis, and documentation
- Ability to craft effective AI prompts for test scripts, SQL queries, and validation logic; awareness of AI limitations and critical review of AI outputs
Nice-to-Haves- Familiarity with Databricks notebooks or Lakehouse concepts
- Exposure to data validation frameworks (Great Expectations, Deequ, Soda) or data observability tools (Monte Carlo, Bigeye)
- Experience working with DevOps pipelines - Jenkins, GitHub
- Knowledge of automation frameworks, especially Robot Framework with Selenium/Playwright
- Exposure in working and leading AI initiatives
- Ability to reason statistically about datasets - detecting outliers, sampling strategies, and validating metric correctness
- Experience testing financial/banking data APIs
- Experience with AI-powered test platforms (Testim, Mabl, Katalon AI, Applitools) or building custom AI agents for test workflows
- Visual regression testing and accessibility testing (WCAG) for data-driven web applications
Soft Skills- Strong attention to detail with a data accuracy mindset; analytical thinking to investigate complex discrepancies
- Excellent communication, documentation habits, and ability to collaborate in a distributed team
- Curious, proactive learner comfortable with ambiguity and evolving specifications
What You'll Gain- Transition into Data Quality Engineering with hands-on exposure to Databricks, ETL validation, and modern data pipelines
- Real-world experience integrating AI tools into enterprise QA workflows - a rapidly growing skill set
- A supportive environment for learning, cross-training, and influencing enterprise-level data validation automation
- Exposure to financial data domains and industry-grade data quality standards
ApplyingWe know that sometimes the 'perfect candidate' doesn't exist, and that people can be put off applying for a job if they don't meet all the requirements. If you're excited about working for us and have relevant skills or experience, please go ahead and apply. You could be just what we need!
If you need any adjustments to support your application, such as information in alternative formats, special requirements to access our buildings or adjusted interview formats please contact us at [redacted] and we'll do everything we can to help.
