ETL QA

Data Warehousing & ETL Fundamentals:

• Strong understanding of data warehousing
• Expertise in ETL/ELT processes, including data extraction, transformation, loading, and data quality checks
• Familiarity with data integration methodologies (batch processing, real-time processing, data streaming)

• Programming & Scripting:

• Proficiency in SQL (including advanced SQL, window functions, subqueries)
• Expertise in at least one scripting language (Python - Preferred) for data manipulation and automation
• Knowledge of other relevant languages (Scala, Java) is a plus

• Data Modelling & Design:

• Ability to analyse business requirements and translate them into effective data models
• Experience with data modelling tools is a plus

• Data Integration Tools:

• Proficiency in at least one ETL/ELT tool (e.g., Informatica PowerCenter, Talend, Apache NiFi, AWS Glue, Azure Data Factory)
• Experience with cloud-based data integration services (AWS Glue, Azure Data Factory, Google Cloud Dataflow) is a plus

• Cloud Technologies:

• Familiarity with cloud platforms (AWS/Azure) and their data services (e.g., cloud storage, data lakes, data warehouses)

• Big Data Technologies:

• Experience with big data technologies (Hadoop, Spark, Hive, HDFS) is a plus
• Familiarity with NoSQL databases (e.g., MongoDB, Cassandra) is a plus