AIA Coimbatore
Experience : 6 to 9 years
Job Summary
Contribute as a senior developer specializing in PySpark and Palantir Foundry to build scalable data pipelines and analytical solutions within a global enterprise environment. Collaborate with cross functional teams in a hybrid work setup to transform complex business requirements into reliable data products that improve decision making and operational efficiency.
Responsibilities
- Design robust PySpark data pipelines that reliably process large scale structured and unstructured datasets to enable accurate reporting and analytics for business stakeholders.
- Develop optimized transformations in PySpark that improve runtime performance and resource utilization while maintaining high standards of data quality and consistency.
- Implement modular data workflows in Palantir Foundry that integrate diverse enterprise data sources and provide curated datasets for downstream applications.
- Configure and manage datasets transformations and schedules in Palantir Foundry to ensure that critical data assets remain fresh traceable and well documented.
- Collaborate with product owners data analysts and other developers to translate business requirements into clear technical specifications and reusable data components.
- Conduct detailed code reviews for PySpark and Foundry transformation logic to uphold coding standards improve maintainability and reduce production issues.
- Troubleshoot complex data pipeline incidents by performing root cause analysis and implementing sustainable fixes that prevent recurrence and protect service reliability.
- Optimize data models and query patterns so that analytical and operational dashboards perform efficiently and deliver timely insights to decision makers.
- Document data lineage business rules and transformation logic in a clear and accessible manner so that teams across the organization can confidently reuse shared data assets.
- Partner with platform and infrastructure teams to ensure that Spark cluster configurations job schedules and resource allocations align with performance and cost objectives.
- Apply secure coding and data handling practices to safeguard sensitive information and comply with internal policies and external regulatory expectations.
- Provide mentoring and guidance to less experienced developers on PySpark patterns testing approaches and best practices for building reliable data solutions.
- Engage in continuous improvement activities by evaluating new features in Palantir Foundry and Spark technology ecosystems to enhance the resilience and scalability of existing solutions.
- Coordinate with testing teams to define test data strategies validation rules and automated checks that verify correctness of complex data transformations before production deployment.
- Communicate progress risks and technical constraints clearly to project stakeholders so that delivery timelines and scope can be managed effectively.
- Partner with business teams to identify opportunities where advanced data engineering on Spark and Foundry can streamline processes and create measurable value for customers and communities.
- Ensure that hybrid working practices are effective by using collaboration tools regular check ins and documented workflows that support both in office and remote team members.
- Align daily development activities with the organization mission by focusing on data capabilities that drive better services improved sustainability and responsible use of technology.
Qualifications
- Demonstrate six to eight years of hands on experience in designing and implementing data engineering solutions using PySpark in large scale enterprise environments.
- Exhibit strong proficiency in Palantir Foundry including building transformations managing datasets configuring schedules and integrating with upstream and downstream systems.
- Apply solid understanding of distributed data processing concepts such as partitioning caching and shuffle optimization to tune PySpark jobs for performance.
- Use practical knowledge of SQL and data modeling to design schemas joins and aggregations that support both analytics and operational use cases with high data quality.
- Show proficiency in version control and collaborative development practices so that changes to PySpark and Foundry assets are traceable reviewable and reversible.
- Employ experience with testing frameworks and data validation techniques to establish automated checks that secure reliability of production data pipelines.
- Display excellent communication and collaboration skills that enable effective work with cross functional teams in a hybrid work environment without requiring frequent travel.
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
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