Senior Data Engineer

About Tiger Analytics

Shape the Future of AI. Transform the World's Leading Businesses.

Tiger Analytics is one of the fastest-growing AI services firms, partnering with some of the world's largest and most influential organizations to redefine how business gets done. We help Fortune 500 companies harness the power of Artificial Intelligence, Generative AI, Data Science, Data Engineering and Advanced Analytics to solve their most complex challenges, unlock new growth opportunities, and create lasting business impact.

We are at the forefront of the AI revolution, helping enterprises move beyond traditional analytics to embrace Generative AI, agentic AI, intelligent automation, and cloud-native data platforms. A global presence across North America, Latin America, Europe, and India.

At Tiger Analytics, we don't just help businesses adapt to the future-we help them build it.

What you will be doing

We are looking for a Senior Azure Data Engineer with strong expertise in Microsoft Azure data services, Python, Scala, and Apache Spark. The ideal candidate will design, develop, and maintain scalable data pipelines and ETL/ELT solutions to support enterprise data platforms.

Key Responsibilities:

• Design, develop, and maintain scalable data pipelines using Azure data services, Python, Scala, and Apache Spark.
• Develop and optimize ETL/ELT processes for data ingestion, transformation, and processing across diverse data sources.
• Build and manage data pipelines using Azure Data Factory, Azure Databricks, and Azure Data Lake Storage.
• Develop and optimize Spark applications to improve data processing performance, scalability, and reliability.
• Work with Hadoop and related big data technologies where applicable.
• Monitor pipeline execution, troubleshoot failures, and resolve data processing issues to ensure reliable data delivery.
• Implement data validation, error handling, and data quality checks to ensure accuracy and consistency.
• Collaborate with data architects, analysts, and cross-functional teams to understand requirements and deliver reliable data solutions.
• Follow coding standards, testing practices, version control, and CI/CD procedures.
• Support production deployments, incident resolution, performance tuning, and ongoing maintenance of data engineering solutions.