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 collaborative 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.

• Strong proficiency in Python, SQL, and Advanced Excel for data manipulation and validation• Hands-on experience with Azure Cloud ecosystem, especially:• Azure Data Factory• Azure Data Lake• Azure Databricks• Strong working knowledge of Apache Spark (PySpark preferred)• Experience building and optimizing scalable ETL/ELT data pipelines• Experience implementing CI/CD pipelines (Azure DevOps or similar tools)• Understanding of data architecture, data modelling, and performance tuning________________________________________Key Responsibilities• Design and develop scalable data Products in Azure Cloud.• Manage ingestion, transformation, and validation of multi-source datasets• Collaborate with Data Science teams to deliver model-ready datasets for MMM and advanced analytics• Implement and maintain CI/CD pipelines for data workflows• Optimize Spark jobs and cloud infrastructure for performance and cost efficiency• Ensure data governance, documentation, and best engineering practices• Contribute to cloud architecture decisions and continuous platform improvements.

• B.Tech / B.E. (Computer Science, IT, or related field)• M.Tech / MCA (Preferred)• M.Sc. (Statistics / Mathematics / Data Science)