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.
Required skills and experience
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.
