Description

Key Skills: Pyspark, Databricks, Spark, Python, Cloud (AWS / Azure / GCP)

Roles and Responsibilities:

• Design and build scalable batch and streaming data pipelines for enterprise data platforms
• Develop and optimize Spark and PySpark jobs for large-volume data processing
• Implement data transformations and curated datasets within Databricks environments
• Integrate data from multiple source systems into cloud-based data platforms
• Support data architecture decisions for reliability, scalability, and maintainability

Skills Required:

• 6.5 to 14.2 years of relevant data engineering experience
• Strong hands-on experience with Spark, PySpark, Python, and Databricks
• Experience designing data pipelines and working with distributed data processing platforms
• Ability to handle large-scale datasets with strong performance and scalability focus
• Experience with cloud-based data environments and production-grade data integration workflows

Good to Have:

• AWS, Azure, or GCP experience
• Delta Lake, Kafka, and Airflow
• Cloud data services and data architecture exposure

Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field

Key Skills

Education

Any Graduate