Data Quality Engineer opportunity

Kroger
Data Quality Engineer opportunity
Visa: GC PERM or USC
Rate: please submit your candidate at your best rate
Terms: I don't know if this will be long term contract or C2H yet. We will find out in a meeting with the HM tomorrow.
Process: The first step on this process is a Koncert (Kroger) one way prescreen. We have limited submittal spots for the prescreen. I will be doing all initial conversations about the role on MS Teams Video - recording with candidates in order to present them to get the submittal spot.
PREFERRED LOCATION: Cincinnati, OH
Second preference: Charlotte, NC. Boca Raton, FL, and Chicago, IL
May follow a hybrid schedule (2x a week) in these locations, but manager is also open to remote. More details to come tomorrow . I will review extremely strong candidates outside these locations, but prioritize candidates in these locations.
Top skills needed: testing, SQL, Azure Databricks, and Spark/PySpark
Overview:
We are seeking a skilled Data Quality Engineer to join our team and play a critical role in ensuring the reliability, accuracy, and consistency of data within our warehouse and manufacturing data platform in the Kroger supply chain space. While the title may suggest a focus on quality assurance, this role requires a blend of data engineering and testing expertise. The Data Quality Engineer will be responsible for implementing robust test frameworks, writing automation test cases, and collaborating closely with data engineers to validate and verify the functionality of data pipelines and processes. This is a key role in maintaining the integrity of our data platform and driving continuous improvement in data quality standards.
Technical Skills:

• Proficiency in data engineering technologies such as Azure Databricks, Synapse Analytics, and SQL Hyperscale.
• Strong programming skills in Python and PySpark for developing automation test scripts.
• Experience with building and implementing test frameworks for data-centric applications in Azure cloud environments.
• Knowledge of data quality assurance methodologies and best practices.
• Familiarity with version control systems and CI/CD pipelines for automated testing and deployment.
• Understanding of data modeling, ETL processes, and data warehousing concepts.