1. 4+ years of experience in data engineering, with strong focus on PySpark/python for big data processing.
2. Expertise in building data pipelines and ingestion frameworks from relational, semi-structured (JSON, XML), and unstructured sources (logs, PDFs).
3. Proficiency in Python with solid knowledge of data processing libraries.
4. Robust SQL skills for querying and validating data in platforms like Amazon Redshift, PostgreSQL, or similar.
5. Experience with distributed computing frameworks (e.g., Spark on EMR, Databricks).
6. Familiarity with workflow orchestration tools (e.g., AWS Step Functions, or similar).
Solid understanding of data lake / data warehouse architectures and data modeling basics.