Data Engineer ( AWS & Databricks)

Job highlights

• 3+ years hands-on Databricks, 5+ years AWS data engineering, strong Python and Scala skills
• Design and maintain scalable data pipelines using Databricks and AWS, develop ETL processes, orchestrate workflows, optimize Databricks jobs, collaborate with teams, perform root cause analysis, document processes, work in Agile with JIRA

Job Description:

We are seeking an experienced and hands-on Data Engineer with strong expertise in AWS and Databricks to join our growing data engineering team. The ideal candidate will be passionate about building scalable data pipelines and delivering high-quality data solutions.

Responsibilities:

• Design, develop, and maintain scalable data pipelines and ETL processes using Databricks and AWS services
• Hands-on development using Python, Scala, and SQL
• Orchestrate workflows and manage job scheduling
• Optimize and monitor Databricks jobs for performance and reliability
• Work with large-scale datasets and implement best practices for data ingestion and processing
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions
• Root cause analysis and performance tuning
• Document data engineering processes and developer guides
• Work in an Agile environment using tools like JIRA
• Batch ETL experience is essential; real-time pipeline experience is a plus
• Exposure to Google Cloud Platform (GCP) is a plus

Required Skills & Experience:

• 3+ years of hands-on experience with Databricks
• 5+ years of experience with AWS data engineering services
• Strong proficiency in Python and Scala
• Experience with ETL development and SQL
• Knowledge of workflow orchestration tools
• Experience with monitoring, tuning, and debugging Databricks jobs
• Ability to perform root cause analysis and improve data quality/processes
• Experience working in Agile teams and using JIRA