DataBricks - Data Engineering

Wipro Limited (NYSE: WIT, BSE: 507685, NSE: WIPRO) is a leading technology services and consulting company focused on building innovative solutions that address clients’ most complex digital transformation needs. Leveraging our holistic portfolio of capabilities in consulting, design, engineering, and operations, we help clients realize their boldest ambitions and build future-ready, sustainable businesses. With nearly 245,000 employees and business partners across 65 countries, we deliver on the promise of helping our clients, colleagues, and communities thrive in an ever-changing world. Wipro is an Equal Employment Opportunity employer and makes all employment and employment-related decisions without regard to a person's race, sex, national origin, ancestry, disability, sexual orientation, or any other status protected by applicable law.

Website

http://www.wipro.com

Mandatory Skills: DataBricks - Data Engineering .

Experience: 5-8 Years .

Reinvent your world. We are building a modern Wipro. We are an end-to-end digital transformation partner with the boldest ambitions. To realize them, we need people inspired by reinvention. Of yourself, your career, and your skills. We want to see the constant evolution of our business and our industry. It has always been in our DNA - as the world around us changes, so do we. Join a business powered by purpose and a place that empowers you to design your own reinvention.

Key Responsibilities

          Design and develop scalable data pipelines using Databricks, PySpark, SQL, and Delta Lake.

• Build CDC, batch, and streaming ingestion frameworks for enterprise-scale workloads.
• Implement and manage Medallion Architecture (Bronze, Silver, Gold).
• Develop governance solutions using Unity Catalog, including row-level security, data masking, and fine-grained access controls.
• Build and maintain Data Quality, Monitoring, and Observability frameworks.
• Implement CI/CD pipelines using Databricks Asset Bundles (DABS), Git, and Infrastructure as Code.
• Optimize Databricks clusters, SQL Warehouses, and workloads for performance and cost efficiency.
• Enable self-service analytics through semantic layers, metric views, governed datasets, and certified data products.
• Partner with platform, security, analytics, and business teams to drive adoption of Databricks capabilities.
• Participate in architecture discussions, platform modernization initiatives, and stakeholder workshops.