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.