- 3-5 years of professional IT/Data Engineering experience.
- At least 2 years of strong hands-on experience with Python and SQL.
- Meaningful hands-on experience with Apache Spark/PySpark.
- Production experience with Databricks is strongly preferred.
- Practical experience with AWS or Azure or GCP data services is required.
- Experience developing production ETL/ELT pipelines.
- Experience working with large datasets and distributed data-processing systems.
- Exposure to production support and troubleshooting.
The ideal candidate should:
- Be a strong hands-on engineer rather than someone with only theoretical knowledge.
- Be comfortable switching between Databricks and AWS/Azure/GCP-oriented projects.
- Be able to understand an existing architecture and independently implement assigned components.
- Have strong debugging and problem-solving skills.
- Be comfortable learning unfamiliar technologies when required by a project.
- Have good communication skills for interaction with customers, architects, leads, and internal teams.
- Be capable of explaining technical decisions and implementation approaches.
- Write clean, maintainable, reusable, and testable code. Understand production reliability, security, scalability, and maintainability considerations.
The preferred candidate should have the following core combination: Python + SQL + PySpark/Spark + Databricks + AWS/Azure/GCP + ETL/ELT