1. Knowledge of Spark and Delta: The candidate should have experience and familiarity with using Spark and Delta for data processing and analysis.
2. Technical depth: The hiring manager expects the candidate to possess a strong technical understanding and depth in areas such as databases, testing, and performance tuning.
3. Data modelling concepts: The candidate should have a good understanding of data modelling principles and concepts.
4. Experience with SQL Server/Synapse: Knowledge and experience working with SQL 
Server or Synapse (formerly known as SQL Data Warehouse) is desired.
5. Familiarity with unit testing: The candidate should have knowledge and experience in implementing unit testing practices for their code, rather than relying solely on manual testing.
6. Python skills: Proficiency in Python programming is expected, as it is used for SQL based transformations and other tasks.
7. CI/CD knowledge: Understanding of Continuous Integration/Continuous Deployment (CI/CD) processes and its implementation is desired.
8. Experience with performance tuning: The candidate should have knowledge and practical experience in optimizing performance for data processing tasks.
9. Understanding of indexing and partitioning: Familiarity with indexing and partitioning concepts for optimizing data access and query performance is expected.
10. Familiarity with ADF pipelines: The candidate should be comfortable with Azure Data Factory (ADF) pipelines and have experience in monitoring and managing them.
11. Additional knowledge of CDL, Python-based Spark, and monitoring ADF pipelines may be considered a bonus.