Tech Stack

Description

You will lead the design and implementation of comprehensive data quality testing strategies for our data platforms and pipelines, ensuring data integrity, accuracy, and reliability across the data ecosystem. You will collaborate with data engineers, data scientists, and analysts to understand data requirements and quality standards, and mentor junior QA engineers.

Requirements

• Bachelor’s or Master’s degree in computer science, Business Administration, or comparable professional experience
• Minimum 5 years of data quality assurance expertise within healthcare technology companies or health insurance organizations
• Proficient in complex data transformation and manipulation techniques
• Advanced SQL expertise with proven experience in data integration and validation
• Comprehensive understanding of ETL/ELT workflows and quality assurance methodologies for data processing validation
• In-depth expertise in data pipeline architecture and implementing quality control frameworks
• Practical experience with Spark SQL for distributed data processing
• Extensive knowledge of healthcare data domains including member enrollment, medical claims processing, and prescription drug claims
• Outstanding verbal and written communication abilities with demonstrated success in managing multiple priorities and delivering on tight timelines
• Exceptional collaborative and relationship-building capabilities
• Self-directed professional who excels both as an individual contributor and team player, with proven mentoring experience
• Hands-on experience with scripting languages, particularly Python and Java
• Comprehensive expertise in AWS cloud services including S3, EC2, Systems Manager, Athena, and Databricks platform
• Thorough understanding of software testing methodologies, debugging techniques, and quality assurance protocols

Responsibilities

• Design and implement end-to-end data quality testing strategies for data pipelines, ETL/ELT processes, and data warehouses
• Develop automated data validation frameworks to verify data accuracy, completeness, and consistency
• Create and maintain data quality metrics, monitor dashboards, and alert mechanisms
• Perform complex SQL queries to validate data transformations and business logic
• Design test cases for data ingestion, processing, and consumption layers
• Collaborate with data engineers, data scientists, and analysts to understand data requirements and quality standards
• Lead root cause analysis for data quality issues and implement preventive measures
• Mentor junior QA engineers and establish data testing best practices