Should have strong knowledge in GCP BigQuery and SQL.
Should be proficient with Python or R for data analysis.
Should have Strong knowledge and proven experience of data mining algorithms and statistical analysis.
Should have Ability to interpret complex data and present insights clearly.
Responsibilities
- Design and implement robust data pipelines on cloud platforms using Cloud Composer and Cloud Dataproc to process large scale payer domain datasets with high reliability and performance.
- Develop optimized PySpark and Python code to transform cleanse and aggregate complex healthcare data while maintaining strict data quality and accuracy standards.
- Build efficient data models and schemas in BigQuery and Cloud SQL that support analytics reporting and downstream applications for payer focused use cases.
- Optimize BigQuery queries and storage strategies to reduce cost improve execution time and enhance user responsiveness for business critical workloads.
- Collaborate with product owners data analysts and business stakeholders from the payer domain to translate analytical needs into scalable technical solutions.
- Implement monitoring alerting and automated recovery for Cloud Composer workflows and data pipelines to ensure stability and predictable operations in day to day execution.
- Apply healthcare payer domain knowledge to design data transformations reference mappings and validation rules that align with regulatory clinical and financial requirements.
- Perform code reviews provide constructive feedback and maintain coding standards to ensure maintainable secure and well documented PySpark and Python solutions.
- Troubleshoot production issues across BigQuery Cloud SQL Cloud Composer and Cloud Dataproc environments to restore services quickly and prevent recurrence.
- Create and maintain technical documentation for data flows pipeline configurations and data models to support transparency audit readiness and knowledge sharing.
- Partner with data security and compliance teams to embed privacy access controls and governance practices into all payer data solutions.
- Contribute to continuous improvement by evaluating new cloud features proposing performance enhancements and refining existing data engineering patterns.
- Mentor peers on best practices in cloud based data engineering to uplift team capability and improve delivery quality for payer clients.
Qualifications
- Demonstrate strong hands on experience in designing and developing data pipelines with Cloud Composer and Cloud Dataproc in production grade environments.
- Exhibit advanced proficiency in PySpark and Python for building scalable data processing jobs including tuning and optimization for large healthcare datasets.
- Show proven expertise in BigQuery and Cloud SQL for data modeling query optimization and secure data access suitable for payer analytics scenarios.
- Bring solid understanding of payer domain concepts such as claims eligibility members and providers to design context aware data solutions.
- Display experience working within a hybrid work model and collaborating with distributed teams using agile methods and up-to-date development practices.
- Apply knowledge of data quality testing strategies and version control to deliver reliable well validated and maintainable code for long term use.