Data Analyst – Power BI & Azure

Company Description

👋🏼 We're Nagarro. We are a digital product engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale — across all devices and digital mediums, and our people exist everywhere in the world (18 000+ experts across 39 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in! By this point in your career, it is not just about the tech you know or how well you can code. It is about what more you want to do with that knowledge. Can you help your teammates proceed in the right direction? Can you tackle the challenges our clients face while always looking to take our solutions one step further to succeed at an even higher level? Yes? You may be ready to join us.

Job Description

We are seeking a skilled Data Engineer to join our Business Analytics & Insights (BAI) team. In this role, you will work closely with cross-functional teams to deliver data solutions across domains like Supply Chain, Finance, Operations, Customer Experience, HR, Risk Management, and Global IT.

Key Responsibilities:

• Design and build scalable data pipelines using Azure Data Factory and Databricks
• Ingest, transform, and store data from multiple sources in Azure Data Lake
• Develop reusable frameworks for data ingestion and transformation
• Ensure data quality and implement validation/cleansing mechanisms
• Support migration projects and resolve technical challenges
• Stay current with cloud and data engineering best practices
• Power BI (Strong expertise)
• Experience with Azure Databricks, Data Factory, and Azure Data Lake
• Knowledge of PySpark and data modeling
• Experience with streaming/event-based data ingestion
• Microsoft Certified: Azure Data Engineer Associate

Qualifications

Must-Have Skills:

• Sql (Strong)

• Python (Strong)

Nice-To-Have Skills:

• Familiarity with Jupyter or Databricks Notebooks
• Exposure to data science and machine learning environments