1. Apache Spark:

 Extensive experience in designing, implementing, and optimizing Spark-based

data processing workflows.

 Strong knowledge of Spark's core concepts, RDDs, DataFrames, and Spark SQL.

 Ability to leverage Spark for large-scale data processing and analytics.

2. Python:

 Proficient in Python programming with a focus on data processing and

manipulation.

 Experience using Python libraries for data analysis, such as Pandas and NumPy.

 Familiarity with building and deploying Python-based data pipelines.

3. Trino:

 Hands-on experience with Trino (formerly PrestoSQL) for fast and scalable query

processing.

 Knowledge of Trino architecture and optimization techniques for efficient data

querying.

4. Presto:

 Previous exposure to Presto for distributed SQL queries on large datasets.

 Ability to design and implement Presto-based solutions for complex data access

requirements.

5. Data Lakes:

 Understanding of the principles and best practices for managing and utilizing

data lakes.

 Experience in working with data lakes for storing and retrieving diverse datasets.