Position: Senior Quality Engineer – Data Platform
Work Location: Dallas, TX & San Antonio, TX
Work Mode: Closer to one of these locations, requires to be onsite 3 days a week
Duration: Minimum 1 Year
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.