Quality Assurance Engineer

Curinos empowers financial institutions to put customers at the center of every decision. Our AI-first platform transforms proprietary data, advanced analytics and deep financial services expertise into timely recommendations - delivered right where teams work. The result: confident decisions, stronger customer relationships, and lasting, profitable growth. 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 Description Curinos 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. Responsibilities 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 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 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 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 & Expertise QA & 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 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 Why work at Curinos? Competitive benefits, including a range of Financial, Health and Lifestyle benefits to choose from Flexible working options, including home working, flexible hours and part‑time options, depending on the role requirements – please ask! Unlimited PTO policy, floating holidays and volunteering days and a day off for your birthday Learning and development tools to assist with your career development Work with industry leading Subject Matter Experts and specialist products Regular social events and networking opportunities Collaborative, supportive culture, including an active DE&I program Employee Assistance Program which provides expert third‑party advice on wellbeing, relationships, legal and financial matters, as well as access to counselling services Inclusivity at Curinos We believe strongly in the value of diversity and creating supportive inclusive environments where our colleagues can succeed. As such, Curinos is proud to be an Equal Opportunity Employer. We do not discriminate on the basis of race, color, ancestry, national origin, religion, or religious creed, mental or physical disability, medical condition, genetic information, sex (including pregnancy, childbirth, and related medical conditions), sexual orientation, gender identity, gender expression, age, marital status, military or veteran status, citizenship, or other protected characteristics. #J-18808-Ljbffr