AWS Data Engineer

Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world's leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.). Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 1000, and the Fortune 500 and we are among the top performing and fastest growing companies in the world.

Technical Lead role requires nine to twelve years of experience in designing and delivering data solutions using PySpark Python AWS Databricks and Snowflake. The role focuses on crafting scalable data platforms optimizing data pipelines and ensuring robust analytics capabilities that support enterprise decision making and create measurable value for healthcare and payer oriented initiatives.

• Lead complex data engineering initiatives using PySpark and Python to design build and optimize resilient data pipelines that process large volume datasets for critical business analytics and reporting needs.

• Oversee end to end implementation of cloud based data solutions on AWS by configuring services managing environments and aligning architecture with enterprise standards to ensure longevity and operational efficiency.

• Provide deep technical guidance on Databricks usage to standardize notebooks jobs and cluster configurations that support collaborative development reproducible workflows and efficient resource consumption across teams.

• Drive creation of performant Snowflake data models including fact and dimension structures that enable consistent data consumption reliable metrics definitions and scalable query performance for analytics stakeholders.

• Coordinate migration of legacy data processes into modern cloud and lakehouse architectures to reduce maintenance effort improve reliability and align platforms with current security and compliance expectations.

• Execute rigorous data quality checks validation routines and exception handling strategies so that analytical outputs remain trustworthy traceable and suitable for regulatory and executive consumption.

• Align technical solutions with payer related business processes by translating domain requirements into robust data transformations and ensuring that claims and member data are handled with accuracy and integrity.

• Collaborate closely with product analytics and operations partners to prioritize backlog items refine technical acceptance criteria and deliver features that directly advance organizational goals and social impact in healthcare.

• Optimize job scheduling resource allocation and code performance across PySpark Databricks and Snowflake environments to achieve predictable runtimes cost efficiency and reliable service levels for data consumers.

• Implement secure data access practices on AWS and Snowflake by configuring roles policies and encryption approaches that protect sensitive information while still enabling governed self service analytics.

• Document solution architectures pipeline flows and operational procedures in clear and accessible formats so that cross functional teams can understand dependencies support systems and plan future enhancements.

• Support incident analysis and root cause resolution for data pipeline failures or performance degradation using systematic investigation techniques to restore service quickly and prevent recurrence.

• Coordinate hybrid work cadence with onsite and remote collaboration practices that maintain strong communication timely project updates and dependable delivery in a no travel environment.

• Apply extensive experience with PySpark and Python to craft maintainable modular data engineering solutions that follow sound coding…