Data QA Engineer
On-siteNepal, Punjab Province, Islamic Republic of Pakistan
Job Summary
Design, develop, and maintain automated data quality validation frameworks, including rules engines, anomaly detection, and monitoring for completeness, conformity, integrity, and timeliness. Build and maintain automated test procedures for healthcare data ingestion, transformation, and downstream applications while investigating defects to drive remediation. Collaborate with Engineering, Project Management, Operations, and Connector Engineering teams to translate business and compliance rules into technical test plans and validation logic. Develop QA automation scripts and dashboards using SQL, Python, Java, and cloud-native tools to support system verification protocols and cross-functional design meetings. Produce documentation including test plans, validation criteria, rule catalogs, and QA runbooks while ensuring data security processes align with PHI handling, HIPAA, and SOC 2 requirements.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Information Systems, Data Analytics, or related technical field
- 3–5+ years of experience in Data Quality, Data Engineering, or QA roles in healthcare technology or payer/provider environments
- Strong SQL expertise, including data manipulation, data validation, and profiling at scale
- Experience working with healthcare data types such as enrollment, medical claims, pharmacy claims, provider data, or non‐traditional health and wellness datasets
- Hands-on experience with automation scripting in Python or Java
- Experience with cloud computing environments such as AWS (S3, EC2, SSM, Athena) and Databricks
- Experience with data integration workflows, ETL/ELT pipelines, data mapping, and QA testing protocols
- Experience building automated QA applications, dashboards, or custom rule frameworks
- Ability to analyze complex datasets, identify quality issues, and generate actionable insights
- Excellent communication skills with the ability to work cross-functionally and independently
- Strong organizational skills to manage multiple priorities and deadlines
- Location: On-site
- Standard hours: 9 hours/day, 5 days/week
- Work time: 1 PM - 10 PM (Specific working hours may vary based on business needs.)
Desired Qualifications
- Exposure to Delta Lake, Spark, Airflow, dbt, or event‐driven architectures
- Knowledge of schema evolution management (Parquet, Avro, ORC, JSON)
- Experience with advanced data quality lifecycle management in large‐scale cloud systems
- Familiarity with Terraform, DevOps pipelines, CI/CD workflows, Git‐based version control
- Background in software debugging, system testing methodologies, or performance testing
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