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 seeking a Senior Data Engineer to join our advanced analytics and AI team supporting some of our large enterprise clients. In this role, you will be responsible for designing, developing, and maintaining scalable data pipelines, processing large datasets, and managing scheduled data workflows to support enterprise data engineering initiatives.

Key Responsibilities:

• Design, develop, and maintain scalable data pipelines using Databricks, Apache Spark, Python, and Scala.
• Develop and optimize ETL/ELT processes for data ingestion, transformation, and processing across diverse data sources.
• Work with Hadoop and its ecosystem to process and manage large volumes of structured and unstructured data.
• Develop, troubleshoot, and optimize Spark applications to improve performance, scalability, and reliability.
• Use Control-M to schedule, monitor, and manage batch jobs, workflow dependencies, and data processing pipelines.
• Troubleshoot job failures, resolve data pipeline issues, and ensure timely completion of scheduled workloads.
• Implement data validation, error handling, and quality checks to ensure data 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 deployment procedures.
• Support production deployments, incident resolution, and ongoing maintenance of data engineering solutions.