Company Description WNS Holdings Limited NYSE WNS is a leading Business Process Management BPM company We combine our deep industry knowledge with technology and analytics expertise to co-create innovative digital-led transformational solutions with clients across 10 industries We enable businesses in Travel Insurance Banking and Financial Services Manufacturing Retail and Consumer Packaged Goods Shipping and Logistics Healthcare and Utilities to re-imagine their digital future and transform their outcomes with operational excellence We deliver an entire spectrum of BPM services in finance and accounting procurement customer interaction services and human resources leveraging team-oriented models that are tailored to address the unique business challenges of each client We co-create and execute the future vision of 400 clients with the help of our 44 000 employees Position Overview We are seeking a highly skilled Senior Data Engineer - Python PySpark Azure Databricks to join our dynamic data engineering team This role focuses on building scalable high-performance data pipelines using Python and PySpark within the Azure Databricks environment While familiarity with broader Azure services is valuable the emphasis is on distributed data processing and automation using modern big data frameworks Prior experience in the Property Casualty P C insurance industry is a strong plus Key Responsibilities Data Pipeline Development Optimization Design develop and maintain scalable ETL ELT data pipelines using Python and PySpark Leverage Azure Databricks to process large volumes of structured and semi-structured data efficiently Implement data quality checks error handling and performance tuning across all stages of data processing Data Architecture Modeling Contribute to the design of cloud-based data architectures that support analytics and reporting use cases Develop and maintain data models that adhere to industry best practices and support business requirements Work with Delta Lake Bronze Silver Gold data architecture patterns and metadata management strategies Cloud Integration Azure Integrate and orchestrate data workflows using Azure Data Factory Azure Blob Storage and Event Hub where applicable Optimize cloud compute resources and manage cost-effective data processing at scale Collaboration Stakeholder Engagement Partner with data analysts data scientists and business users to understand evolving data needs Collaborate with DevOps and platform teams to ensure reliable secure and automated data operations Participate in Agile and contribute to sprint planning demos and retrospectives Documentation Best Practices Maintain clear and comprehensive documentation of code pipelines and architectural decisions Contribute to internal data engineering standards and promote best practices for code quality testing and CI CD Qualifications Position Overview We are seeking a highly skilled Senior Data Engineer - Python PySpark Azure Databricks to join our dynamic data engineering team This role focuses on building scalable high-performance data pipelines using Python and PySpark within the Azure Databricks environment While familiarity with broader Azure services is valuable the emphasis is on distributed data processing and automation using up-to-date big data frameworks Prior experience in the Property Casualty P C insurance industry is a strong plus Key Responsibilities Data Pipeline Development Optimization Design develop and maintain scalable ETL ELT data pipelines using Python and PySpark Leverage Azure Databricks to process large volumes of structured and semi-structured data efficiently Implement data quality checks error handling and performance tuning across all stages of data processing Data Architecture Modeling Contribute to the design of cloud-based data architectures that support analytics and reporting use cases Develop and maintain data models that adhere to industry best practices and support business requirements Work with Delta Lake Bronze Silver Gold data architecture patterns and metadata management strategies Cloud Integration Azure Integrate and orchestrate data workflows using Azure Data Factory Azure Blob Storage and Event Hub where applicable Optimize cloud compute resources and manage cost-effective data processing at scale Collaboration Stakeholder Engagement Partner with data analysts data scientists and business users to understand evolving data needs Collaborate with DevOps and platform teams to ensure reliable secure and automated data operations Participate in Agile and contribute to sprint planning demos and retrospectives Documentation Best Practices Maintain clear and comprehensive documentation of code pipelines and architectural decisions Contribute to internal data engineering standards and promote best practices for code quality testing and CI CD