PySpark Data Engineer

• Design, develop, and maintain scalable data pipelines using PySpark on Databricks
• Build and optimize data processing workflows using Python
• Implement workflow orchestration and scheduling (preferably using Airflow, if applicable)
• Work in an Agile delivery environment with cross-functional teams
• Ensure data quality, performance, and reliability of data solutions
• Support integration of data from multiple sources into analytics-ready structures

• 5+ years of hands-on experience in PySpark (Databricks) and Python
• Strong experience in building and maintaining data engineering pipelines
• Exposure to Airflow (preferred, not mandatory)
• Overall 6–8 years of professional experience in data engineering or related roles
• Strong communication skills and ability to work with distributed teams
• Good understanding of Agile development practices

• Develop efficient and scalable big data processing solutions using Py Spark
• Debug, optimize, and enhance existing data workflows and pipelines
• Work independently as well as collaboratively in Agile teams
• Translate business requirements into technical data solutions
• Manage multiple tasks and deliver within deadlines in a fast-paced environment

• Opportunity to work on modern data engineering stack including Databricks and Python
• 6-month engagement duration with potential for extension based on performance
• Exposure to large-scale data engineering projects in an international environment
• Agile-driven collaborative working culture

Want to discuss this opportunity in more detail? Feel free to reach out.