About the Role
We are seeking highly skilled Data Engineers with 4+ years hands-on experience in Databricks and strong expertise in data engineering, ETL pipelines, and cloud-based data platforms . The ideal candidate will design, build, and maintain large-scale data processing systems, enabling high-quality data flow and analytics for business insights.
Key Responsibilities
- Design, develop, and optimize ETL / data transformation pipelines using Python, PySpark, and Apache Spark .
- Build and manage scalable data architectures for handling large and complex datasets.
- Work extensively on Databricks and Delta Lake , ensuring efficient data storage and processing.
- Collaborate with cross-functional teams to deliver data solutions across multiple domains.
- Leverage cloud platforms like AWS (Glue, EMR) , Snowflake , Dataiku , and Alteryx for data engineering workflows.
- Implement best practices in data modeling , data warehousing , and data governance for both on-prem and cloud environments.
- Continuously evaluate emerging technologies and tools to enhance data engineering capabilities.
- Troubleshoot, optimize, and ensure performance of data pipelines and analytical systems.
- Take ownership of technical deliverables, contributing to project leadership and independent execution .
Required Skills & Experience
Mandatory : Strong hands-on experience with Databricks and Delta Lake .Proficiency in Python , PySpark , and Apache Spark .Experience in ETL design , data integration , and data pipeline optimization .Solid understanding of data modeling , data warehousing , and distributed systems .Experience working on AWS , Snowflake , Dataiku , or Alteryx platforms.Ability to manage and transform large-scale, multi-dimensional datasets .Strong problem-solving , analytical , and process optimization skills.Up-to-date with modern data engineering tools and cloud data ecosystems .