• Support and improve existing test automation • Identify and troubleshoot defects • Validate new features to ensure system stability, scalability, and compatibility with evolving product functionality • Collaborate with teams to deliver high-quality software solutions • Participate in code reviews and contribute to technical decisions

• Support and improve existing test automation • Identify and troubleshoot defects • Validate new features to ensure system stability, scalability, and compatibility with evolving product functionality • Collaborate with teams to enhance customer service, increase efficiency, and support seamless omnichannel interactions

Role Description We are seeking a hands-on QA Lead to drive quality assurance for a scalable, enterprise-wide data platform for an insurance client. The role involves validating batch and real-time data pipelines, ensuring data accuracy across Raw, Silver, and Gold layers, and supporting report rationalization and self-service analytics (Power BI). The QA Lead will define and implement end-to-end data testing strategies, covering ingestion (AWS Glue, Kinesis), transformation (DBT), and consumption layers, while ensuring data quality, integrity, and performance optimization. Qualifications - Strong experience in data testing / ETL testing / data QA - Hands-on expertise with: - Snowflake (data validation, SQL testing) - DBT (testing, model validation) - AWS Glue (batch pipeline validation) - Amazon Kinesis (real-time pipeline testing) - Advanced proficiency in SQL for data validation and reconciliation - Programming skills in Python (preferred) - Experience in data reconciliation (source vs target) - Experience with data quality frameworks and validation techniques - Experience with automated data testing tools - Understanding of medallion architecture (Raw, Silver, Gold layers) - Experience validating Power BI reports and datasets - Strong understanding of business KPIs and reporting consistency - Experience in the insurance domain (Policy, Claims, Billing data) - Familiarity with regulatory reporting, audit, and reconciliation requirements - 8–12 years in QA / Data Testing / ETL Testing - 3+ years in QA leadership or lead role - Experience working on enterprise-scale data platforms Requirements - Define and implement end-to-end QA strategy for the enterprise data platform - Establish test frameworks, standards, and governance for data validation - Lead QA planning, estimation, and execution across multiple data streams - Validate data across Raw, Silver, and Gold layers ensuring accuracy, completeness, and consistency - Perform source-to-target reconciliation for batch and real-time pipelines - Design and execute: - Data quality checks - Transformation validation (DBT models) - Aggregation and KPI validation - Test batch ingestion pipelines using AWS Glue - Validate real-time streaming data pipelines using Amazon Kinesis - Ensure data latency, sequencing, and event consistency in streaming pipelines - Validate datasets powering Power BI self-service reports - Support report rationalization initiatives by ensuring consistency of KPIs and eliminating redundant data sources - Perform report/data reconciliation testing across legacy vs new platform - Develop and implement automated data testing frameworks - Leverage SQL, Python, and testing tools (e.g., Great Expectations, DBT tests, custom frameworks) - Enable continuous testing integration within CI/CD pipelines - Validate performance of data pipelines and queries in Snowflake - Identify bottlenecks and work with engineering teams to optimize pipelines and queries - Ensure scalability for large data volumes and concurrent workloads - Define and enforce data quality rules, thresholds, and monitoring - Implement data anomaly detection and alerting mechanisms - Ensure compliance with audit, reconciliation, and governance standards Benefits - Inclusive workplace culture - Equal employment opportunity (EEO) and affirmative action employer - Encouragement of individuality and growth - Prioritization of well-being

• Prospectively review all OASIS assessments to ensure accuracy, completeness, and compliance with federal and state home health regulations • Analyze OASIS variation and alert reports to identify documentation gaps and trends • Ensure correct ICD‑10 coding and sequencing, including primary diagnoses and co‑morbidities • Collaborate with home health clinicians to clarify documentation issues and support appropriate corrections • Review visit utilization to ensure services align with patient condition, care guidelines, and reimbursement requirements • Identify potential underutilization, overutilization, or financial risk and report findings to Clinical Supervisor or designee • Communicate problematic trends in OASIS data, outcomes, and processes to leadership • Participate in Quality Improvement (QI), performance improvement, and Corporate Compliance activities • Assist with additional chart audits and clinical documentation reviews as needed