SENIOR DATA QA ENGINEER

• Bachelor's degree in a relevant field such as health science, computer science, business, or equivalent experience.

• 5 years experience with data quality in a healthcare tech provider or a health pan.

• Strong data manipulation skills.

• Strong querying skills with SQL and data integration.

• Strong knowledge of ETL data processing and QA processes to validate ETL process.

• Strong knowledge of data pipelines and data quality processes in data pipelines.

• Working knowledge of Spark SQL.

• Strong knowledge of healthcare enrollment, medical claims, and drug claims data required.

Additional knowledge of non-traditional data types (health & wellness, workforce

productivity, and EMR) preferred.

• Excellent written and verbal communication skills, with the ability to multitask and prioritize projects to meet scheduled deadlines.

• Strong interpersonal skills required.

• Ability to work well independently or in a team environment, and mentor other team members.

• Advanced programming skills including automation systems and databases.

• Familiarity with programming script languages including Java and Python.

• Excellent knowledge of cloud computing (AWS, S3, EC2,SSM, Athena, DataBricks).

• Detailed knowledge of application functions, bug fixing, and testing protocols.

• Very good knowledge of Machine Learning, and AI and their implications on real-time projects.

• Work collaboratively with Project Management, Operations, Development, and Engineering to deliver all contractually required services. Investigate and respond to questions for assigned product lines.

• Provide data quality analysis and data mining on client-specific data and general healthcare data.

• Review testing executed by multiple groups.

• Lead the development of the data quality strategy.

• Assist the Connector Engineering/Tools team with new business requirements and help them with functional specifications.

• Identify, document, and refine standard processes.

• Meet with the software design team to discuss verification protocols.

• Identify software application weaknesses and target areas.

• Sketch out ideas for automated software test procedures.

• Review software bug reports and highlight problem areas.

• Write automation scripts and implement software applications.

• Design and install Automated QA applications and dashboards.

• Identify quality issues and create test reports.

• Collaborate with the design team to solve application faults.