Data Engineering Lead
Leads data engineering delivery and technical teams, designing and operating scalable Databricks-based platforms while advising enterprise customers on architecture, modernization, and solution design.
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 66,000+ employees.
Please Note that this is a 4 Day Work from Office Role in Central London & 1 Day work from Home
Key Responsibilities
Experience
Β
Tech Stack
Leads data engineering delivery and technical teams, designing and operating scalable Databricks-based platforms while advising enterprise customers on architecture, modernization, and solution design.
Lead the design and operation of Azure Databricks lakehouse infrastructure, building reliable data pipelines and quality controls while guiding engineering practices and collaborating across teams to deliver business value.
Leads the technical direction of a cloud-based data platform, scaling ingestion, governance, and machine learning infrastructure while delivering cross-functional initiatives and mentoring engineers.
As a Data Engineer V, you will lead a data engineering team, driving technical direction and ensuring the delivery of high-quality data solutions while mentoring engineers.
Build and operate petabyte-scale data pipelines that filter, retain, move, and offload driving datasets, improving data quality and reducing storage costs while preserving valuable training data.