Senior Quality Engineer - Data Platform

Senior Quality Engineer - Data Platform Mission Own comprehensive, risk-based validation of complex IRIS data-platform capabilities and provide actionable evidence of functional, data, integration, resiliency, and release quality, using production-quality Python automation, reusable validation frameworks, and automated CI/CD quality gates. Key responsibilities

• Design, develop, test, and maintain production-quality Python test automation, applications, reusable components, and validation frameworks that support complex platform quality-engineering initiatives.

• Design risk-based test strategies for complex features, integrations, data flows, and releases.

• Build and maintain scalable automated suites for REST APIs, JSON contracts, ETL/ELT pipelines, transformations, orchestration workflows, Spark-based processing, Kubernetes-based services, and cloud dependencies.

• Apply strong software-design practices, including object-oriented programming, modular design, parameterization, configuration management, structured logging, exception handling, packaging, and debugging, to Python automation frameworks and validation utilities.

• Design complex data-reconciliation approaches that validate source-to-target completeness, accuracy, transformation logic, duplicates, null handling, referential integrity, and exception behavior.

• Lead unit, regression, integration, end-to-end, negative, boundary, failure, recovery, resiliency, performance, and scalability validation for assigned capabilities.

• Create reusable Python frameworks, validation libraries, templates, fixtures, controlled test data, and self-service capabilities that are adopted across workflows and teams.

• Partner with data consumers, application teams, platform engineers, and software engineers to translate requirements and risks into validation approaches and improve the quality of delivered datasets.

• Analyze complex application defects, pipeline failures, performance bottlenecks, and data-quality issues across application, data, orchestration, and environment layers; isolate failure conditions and provide actionable root- cause evidence.

• Validate application and pipeline behavior across supported cloud and Kubernetes configurations, dependencies, scaling conditions, and recovery scenarios.

• Implement and use observability, monitoring, logging, and alerting capabilities to validate data quality, application reliability, and timely pipeline execution across cross-component workflows.

• Maintain traceability among requirements, risks, tests, defects, execution results, and release-quality evidence; identify systemic coverage gaps and drive remediation through test design, engineering improvements, or explicit risk acceptance.

• Embed automated quality gates into CI/CD pipelines and drive cloud-native development, DevOps, and quality- engineering practices across applicable platform components.

• Drive improvements in test automation, pipeline reliability, performance, and operational efficiency through reusable engineering patterns and continuous automation.

• Review test designs and automation code, mentor Quality Engineers, and contribute technical documentation, knowledge sharing, and reusable implementation examples.

• Use AI-assisted software and test development responsibly to accelerate Python coding, test generation, test- data creation, debugging, troubleshooting, and documentation while maintaining documented human review of all generated outputs for correctness, security, maintainability, relevance, and coverage. Expected deliverables

• Risk-based feature and release test strategies.

• Production-quality Python test automation, validation frameworks, and reusable components committed to source control.

• Cross-component automated unit, functional, API, data, regression, integration, and end-to-end test suites.

• Reusable Python frameworks, libraries, fixtures, templates, controlled test data, and self-service validation capabilities adopted across workflows.

• Complex data-reconciliation, resiliency, and failure/recovery validation assets.
• Defect evidence, root-cause analyses, and retest confirmation for complex cross-component issues.
• Requirements-to-test traceability, quality-risk assessments, and release-readiness evidence.
• Automated CI/CD quality gates, observability capabilities, and actionable diagnostic information.
• Test reviews, mentoring outputs, coverage-improvement plans, technical documentation, and knowledge-transfer materials.

• Strong Python software-development and automated quality-engineering experience, including object-oriented programming, classes, modules, functions, parameters, exception handling, reusable component design, and pytest or comparable testing frameworks.

• Proficiency with Python logging, configuration management, command-line parameters, debugging, structured error-handling patterns, dependency management, and Python packaging.

• Experience writing unit, integration, regression, and end-to-end tests for Python applications, automation frameworks, reusable components, and transformation logic.

• Knowledge of software-design principles, maintainable coding practices, source control, code reviews, reusable framework design, and automated quality gates.

• Strong SQL development, query optimization, data validation, and data-reconciliation capabilities.
• Experience with data engineering and the development and testing of ETL/ELT pipelines.
• Strong hands-on experience with Apache Spark or PySpark and distributed data processing.
• Experience using Python for data transformation, test automation, validation, and operational tooling.
• Strong hands-on experience with Apache Airflow or comparable workflow-orchestration platforms.
• Knowledge of data modeling, data warehousing, source-to-target mapping, and transformation concepts.

• Strong REST API testing, JSON validation, data-pipeline testing, negative testing, boundary testing, and failure- recovery validation capabilities.

• Strong hands-on experience validating containerized applications, data workloads, and services in Kubernetes- based environments.

• Strong experience with cloud-native platforms and AWS services; experience with Azure is also beneficial.

• Strong hands-on experience with Git, CI/CD, DevOps practices, automated quality gates, and deployment- validation processes.

• Experience with observability, structured logging, monitoring, alerting, diagnostics, and operational analytics.
• Knowledge of data quality, lineage, governance, traceability, and release-evidence practices.

• Ability to create reproducible defects, isolate failure conditions, perform root-cause analysis, assess risk, and communicate actionable quality evidence.

• Demonstrated commitment to automation, reusable engineering patterns, continuous improvement, and operational excellence.

• Strong hands-on proficiency with data reconciliation, enterprise data-platform testing, resiliency testing, test- framework development, and root-cause analysis.

• Demonstrated ability to lead validation of complex cross-component changes and guide other quality engineers.

• Knowledge of AI-assisted software development, including the effective use of coding copilots and large language models to accelerate development, testing, test-data creation, documentation, debugging, and troubleshooting.

• Demonstrated ability to review and validate AI-generated code, tests, documentation, and recommendations for correctness, security, maintainability, relevance, and coverage. Preferred qualifications

• Experience with non-functional testing, distributed systems, production incident analysis, or data observability.
• Experience creating testing frameworks or common enterprise QE assets.
• Experience communicating release risk to senior technical and product stakeholders.
• Experience working in Agile product teams and vendor-delivered engagements.