Data QA Engineer

Key Responsibilities:

Develop and execute comprehensive test plans to validate data as it moves through the pipeline from Snowflake to other sources and vice versa.
Monitor and validate ETL processes to ensure data is accurately and efficiently loaded.
Automate data validation and data quality checks within the data pipeline using Soda.io, SQL, Python, and Snowflake tools.
Track, document, and manage data quality issues across the data pipeline and within Snowflake.
Collaborate with data engineers and pipeline developers to resolve data discrepancies and enhance data quality.
Document test plans, cases, and outcomes for data processes.
Regression testing of data platform.
Desired Skills:

Must have skills:

Hands-on testing experience with Snowflake, including data modeling, ETL and ELT processes.
Expertise in data validation, query optimization, and data management best practices in data sources/pipeline.
Experience with data pipeline tools and frameworks, and their integration with Snowflake.
Experience with cloud platforms, preferably AWS and their data services.
RDBMS, Advanced SQL (including joins, subqueries and writing complex queries)
Basic knowledge of AWS Airflow and Soda.io.
Basic knowledge on Python scripting.
Added advantage skill set:

AWS Devops CI/CD
Experience with Agile development and delivery – Scrum/Kanban methodologies
Experience within pharma/healthcare sector
Certification in Snowflake (e.g., SnowPro Core)
Experience with other cloud platforms (e.g. Azure, Google Cloud) and their data services.