Quality Assurance Lead

About the role

This position is located at our client site five days a week in either Pittsburgh, PA, Dallas, TX, Cleveland, OH, or Birmingham, AL.

Responsibilities

• Lead the overall quality assurance strategy, test planning, execution, and reporting across large scale data and application initiatives.
• Design, develop, and maintain automated testing frameworks using Python and PySpark for enterprise data validation.
• Validate large scale data pipelines across Hadoop based technologies, ensuring data accuracy, completeness, consistency, and reliability.
• Develop comprehensive end to end test scenarios covering data ingestion, transformation, batch processing, and downstream integrations.
• Perform database validation, reconciliation, and data quality testing using Oracle PL/SQL and SQL.
• Validate machine learning data pipelines and model workflows, including experiment tracking and data integrity validation.
• Monitor and validate enterprise workflow scheduling processes utilizing enterprise job schedulers.
• Integrate automated testing into CI/CD pipelines using Jenkins, Git, and modern DevOps practices.
• Develop Unix/Linux scripts to support automation, batch validation, and operational testing.
• Lead defect management activities including root cause analysis, prioritization, and resolution tracking.
• Ensure testing activities align with enterprise governance, audit, compliance, and data security standards.
• Mentor QA engineers and champion automation, continuous improvement, and quality engineering best practices.

Requirements

• Bachelor's degree in Computer Science, Information Systems, Engineering, or related discipline (or equivalent experience).
• 8+ years of Quality Assurance or Test Engineering experience, including experience leading QA initiatives.
• Strong programming experience with Python and PySpark.
• Hands on experience testing enterprise Big Data platforms including Hadoop, Hive, Impala, and Sqoop.
• Strong experience validating relational databases using Oracle PL/SQL and SQL.
• Experience working with enterprise workload schedulers such as Control M or similar scheduling platforms.
• Strong Unix/Linux scripting skills.
• Experience implementing QA automation within CI/CD pipelines using Jenkins and Git.
• Understanding of DevOps methodologies and automated software delivery pipelines.
• Experience working with container technologies such as Docker and OpenShift.
• Strong analytical, troubleshooting, and problem solving skills, particularly involving complex data environments.
• Excellent communication and collaboration skills with technical and business stakeholders.

Qualifications

• To Be Successful In This Role:
• Bachelor's degree in Computer Science, Information Systems, Engineering, or related discipline (or equivalent experience).
• 8+ years of Quality Assurance or Test Engineering experience, including experience leading QA initiatives.
• Strong programming experience with Python and PySpark.
• Hands on experience testing enterprise Big Data platforms including Hadoop, Hive, Impala, and Sqoop.
• Strong experience validating relational databases using Oracle PL/SQL and SQL.
• Experience working with enterprise workload schedulers such as Control M or similar scheduling platforms.
• Strong Unix/Linux scripting skills.
• Experience implementing QA automation within CI/CD pipelines using Jenkins and Git.
• Understanding of DevOps methodologies and automated software delivery pipelines.
• Experience working with container technologies such as Docker and OpenShift.
• Strong analytical, troubleshooting, and problem solving skills, particularly involving complex data environments.
• Excellent communication and collaboration skills with technical and business stakeholders.

Skills

• Python
• PySpark
• Hadoop Ecosystem (HDFS, Hive, Impala, Sqoop)
• Oracle PL/SQL
• Unix/Linux
• Jenkins
• Git
• Docker
• OpenShift
• DevOps Methodologies
• Container Technologies
• Machine Learning
• Data Governance
• Data Lineage
• Regulatory Reporting
• Quality Engineering
• Automation Frameworks
• Database Validation
• Test Automation
• CI/CD Pipelines
• Big Data Testing
• Enterprise Scale Data Platforms
• Distributed Applications
• Financial Services
• Banking
• Highly Regulated Industries
• Financial Crime
• AML
• Fraud Detection
• Risk
• Compliance Technology Platforms
• Cloud Native Data Platforms
• AWS (Glue, Athena, S3, SageMaker)
• Machine Learning Models
• Advanced Analytics Platforms
• Container Technologies
• DevOps Methodologies
• Continuous Integration/Continuous Deployment (CI/CD)
• Automated Software Delivery Pipelines
• Software Development Lifecycle (SDLC)
• Software Testing Lifecycle (STLC)
• Software Quality Assurance (SQA)
• Software Quality Management (SQM)
• Software Quality Engineering (SQE)
• Software Quality Assurance (SQA) Frameworks
• Software Quality Management (SQM) Frameworks
• Software Quality Engineering (SQE) Frameworks
• Software Quality Assurance (SQA) Best Practices
• Software Quality Management (SQM) Best Practices
• Software Quality Engineering (SQE) Best Practices
• Software Quality Assurance (SQA) Processes
• Software Quality Management (SQM) Processes
• Software Quality Engineering (SQE) Processes
• Software Quality Assurance (SQA) Tools
• Software Quality Management (SQM) Tools
• Software Quality Engineering (SQE) Tools
• Software Quality Assurance (SQA) Techniques
• Software Quality Management (SQM) Techniques
• Software Quality Engineering (SQE) Techniques
• Software Quality Assurance (SQA) Metrics
• Software Quality Management (SQM) Metrics
• Software Quality Engineering (SQE) Metrics
• Software Quality Assurance (SQA) KPIs
• Software Quality Management (SQM) KPIs
• Software Quality Engineering (SQE) KPIs
• Software Quality Assurance (SQA) Roadmaps
• Software Quality Management (SQM) Roadmaps
• Software Quality Engineering (SQE) Roadmaps
• Software Quality Assurance (SQA) Strategies
• Software Quality Management (SQM) Strategies
• Software Quality Engineering (SQE) Strategies
• Software Quality Assurance (SQA) Tactics
• Software Quality Management (SQM) Tactics
• Software Quality Engineering (SQE) Tactics
• Software Quality Assurance (SQA) Best Practices
• Software Quality Management (SQM) Best Practices
• Software Quality Engineering (SQE) Best Practices
• Software Quality Assurance (SQA) Processes
• Software Quality Management (SQM) Processes
• Software Quality Engineering (SQE) Processes
• Software Quality Assurance (SQA) Tools
• Software Quality Management (SQM) Tools
• Software Quality Engineering (SQE) Tools
• Software Quality Assurance (SQA) Techniques
• Software Quality Management (SQM) Techniques
• Software Quality Engineering (SQE) Techniques
• Software Quality Assurance (SQA) Metrics
• Software Quality Management (SQM) Metrics
• Software Quality Engineering (SQE) Metrics
• Software Quality Assurance (SQA) KPIs
• Software Quality Management (SQM) KPIs
• Software Quality Engineering (SQE) KPIs
• Software Quality Assurance (SQA) Roadmaps
• Software Quality Management (SQM) Roadmaps
• Software Quality Engineering (SQE) Roadmaps
• Software Quality Assurance (SQA) Strategies
• Software Quality Management (SQM) Strategies
• Software Quality Engineering (SQE) Strategies
• Software Quality Assurance (SQA) Tactics
• Software Quality Management (SQM) Tactics
• Software Quality Engineering (SQE) Tactics
• Software Quality Assurance (SQA) Best Practices
• Software Quality Management (SQM) Best Practices
• Software Quality Engineering (SQE) Best Practices
• Software Quality Assurance (SQA) Processes
• Software Quality Management (SQM) Processes
• Software Quality Engineering (SQE) Processes
• Software Quality Assurance (SQA) Tools
• Software Quality Management (SQM) Tools
• Software Quality Engineering (SQE) Tools
• Software Quality Assurance (SQA) Techniques
• Software Quality Management (SQM) Techniques
• Software Quality Engineering (SQE) Techniques
• Software Quality Assurance (SQA) Metrics
• Software Quality Management (SQM) Metrics
• Software Quality Engineering (SQE) Metrics
• Software Quality Assurance (SQA) KPIs
• Software Quality Management (SQM) KPIs
• Software Quality Engineering (SQE) KPIs
• Software Quality Assurance (SQA) Roadmaps
• Software Quality Management (SQM) Roadmaps
• Software Quality Engineering (SQE) Roadmaps
• Software Quality Assurance (SQA) Strategies
• Software Quality Management (SQM) Strategies
• Software Quality Engineering (SQE) Strategies
• Software Quality Assurance (SQA) Tactics
• Software Quality Management (SQM) Tactics
• Software Quality Engineering (SQE) Tactics
• Software Quality Assurance (SQA) Best Practices
• Software Quality Management (SQM) Best Practices
• Software Quality Engineering (SQE) Best Practices
• Software Quality Assurance (SQA) Processes
• Software Quality Management (SQM) Processes
• Software Quality Engineering (SQE) Processes
• Software Quality Assurance (SQA) Tools
• Software Quality Management (SQM) Tools
• Software Quality Engineering (SQE) Tools
• Software Quality Assurance (SQA) Techniques
• Software Quality Management (SQM) Techniques
• Software Quality Engineering (SQE) Techniques
• Software Quality Assurance (SQA) Metrics
• Software Quality Management (SQM) Metrics
• Software Quality Engineering (SQE) Metrics
• Software Quality Assurance (SQA) KPIs
• Software Quality Management (SQM) KPIs
• Software Quality Engineering (SQE) KPIs
• Software Quality Assurance (SQA) Roadmaps
• Software Quality Management (SQM) Roadmaps
• Software Quality Engineering (SQE) Roadmaps
• Software Quality Assurance (SQA) Strategies