AIA - Weekend Drive - BBSR 04th July 2026
Interview Location: Bhubaneswar
Mode Of Interview : Face to Face
Interview Date: 04th July 2026
Skill: Azure Databricks & Pyspark
Experience - 6 - 9 Years
Job Summary
This hybrid Data Engineer role focuses on designing and optimizing Databricks SQL and PySpark solutions for complex payer and Medicare Medicaid claims processing. The role spans data engineering analytics and quality assurance to ensure accurate claim insights that support compliant healthcare decisions and improved member outcomes.
Responsibilities
Data Engineering - Pyspark Data bricks
- Design scalable Databricks SQL and PySpark solutions that process large volumes of payer and Medicare Medicaid claims data to ensure reliable analytics outputs
- Develop robust data pipelines that ingest transform and aggregate claims information to support timely decision making for healthcare operations
- Optimize PySpark jobs and Databricks SQL queries to improve runtime performance stability and cost efficiency across the hybrid cloud environment
- Implement reusable data models and curated datasets tailored for claims adjudication analytics risk scoring and operational reporting needs
- Collaborate with business and product stakeholders to translate payer and claims requirements into detailed technical solutions that align with organizational goals
- Perform detailed data profiling and validation on Medicare Medicaid claims to identify anomalies gaps and quality issues before downstream consumption
- Create automated testing frameworks for Databricks SQL and PySpark components to reduce defects and maintain consistent quality across releases
- Document end to end data flows transformation logic and technical design decisions to ensure transparency maintainability and effective knowledge sharing
- Partner with analysts and data consumers to design efficient query patterns and dashboards that reveal actionable insights from claims trends and utilization metrics
- Ensure compliance with healthcare data policies by implementing appropriate access controls masking strategies and audit trails within the Databricks environment
- Troubleshoot production issues in Databricks jobs by analyzing logs dependencies and datasets to restore service with minimal impact to downstream users
- Coordinate with infrastructure and platform teams to align cluster configurations libraries and deployment processes with performance and reliability requirements
- Contribute to continuous improvement by recommending new Databricks features coding patterns and automation approaches that enhance team productivity and solution quality
Qualifications
- Exhibit advanced proficiency in Databricks SQL by crafting complex joins aggregations and window functions that support nuanced payer and claims analytic scenarios
- Demonstrate strong PySpark experience through hands on development of distributed data processing pipelines capable of handling large scale Medicare Medicaid claims datasets
- Apply deep understanding of payer operations and claims life cycle to design transformations that respect benefit rules payment logic and regulatory constraints
- Utilize practical knowledge of Medicare and Medicaid programs to ensure that analytic outputs support compliance reporting and value based care initiatives
- Bring eight to twelve years of professional experience in data engineering or advanced analytics roles with a focus on enterprise scale implementations
- Communicate effectively in hybrid work settings by coordinating work progress clarifications and issue resolutions with onsite and remote stakeholders during day shifts
- Adopt structured development practices including version control code reviews and documentation to improve reliability and long term maintainability of solutions
关于高知特 (Cognizant)
高知特(Cognizant)（纳斯达克代码：CTSH）作为一家AI Builder和相关技术服务提供商，致力于通过打造全栈AI解决方案，帮助企业将人工智能投资转化为实际价值。公司凭借深厚的行业经验、流程优化和工程技术专长，将企业独特的业务场景融入科技系统，赋能组织释放人才潜能，推动切实成果，并帮助全球企业在瞬息万变的环境中保持领先。如需了解更多详情，敬请访问 cognizant.ai 或关注@cognizant。
补充雇佣信息
薪酬信息截至本职位发布之日为准。Cognizant 保留在适用法律允许的范围内随时修改该信息的权利。
申请人可能需要通过现场面试或视频会议的方式参加面试。此外，候选人在每次面试时可能需要出示其当前所在州或政府签发的有效身份证件。
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