Role : Quality Engineer (ETL)
Location : Minneapolis, MN (onsite)
Year of experience : 2-3 years only
Required AI Skills : -AI User Persona to leverage approved AI tools to streamline tasks & efficiency.
Apply AI-driven insights to support data-informed decision across functional areas Identify opportunities to embed AI into existing processes CoPilot, github - All contractor resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities.
Role Overview :
EDW (Enterprise Data Warehouse) QE (Quality Engineer) professional is responsible for ensuring the accuracy, integrity, and quality of data within the data warehouse environment.
Their key responsibilities include validating ETL (Extract, Transform, Load) processes using AI tools, verifying source-to-target data mappings, testing data transformations, leveraging AI CoPilot, Python and ensuring that reports and dashboards reflect accurate business information.
They collaborate closely with data engineers, business analysts, and developers to identify data issues, create comprehensive test plans and test cases, and support defect resolution throughout the development lifecycle. Understanding of Bronze/Silver/Gold data arch in platforms (e.g., Databricks, Snowflake, Azure), data visualization tools (e.g., Tableau, Power BI).
Exposure to AI/ML model validation or testing data pipelines that support machine learning workflows & edw concepts (snowflake schema, fact/dimension tables) and data quality, profiling, and reconciliation?.
Required Qualifications: Bachelor's degree in computer science, Information Systems, Engineering, or related field.
5+ years of experience in QA/testing with a focus on data warehouse, ETL, or BI testing.
2-5 years' experience with SQL skills for data validation and test automation.
2-5 years of experience with QA tools like HP ALM, JIRA, ADO or similar.
2-5 years of experience in writing test cases and executing manual and automated tests.
5+ years of experience in QA with a focus on data warehouse, ETL,or BI testing, SQL skills & automation tools and HP ALM, JIRA, ADO or similar.
Strong understanding of Bronze/Silver/Gold data architecture in modern data platforms (e.g., Databricks, Snowflake, Azure Synapse).
Experience with cloud data platforms (e.g., Snowflake) Familiarity with data visualization tools (e.g., Tableau, Power BI).
Scripting experience in Python or Shell for test automation. Experience in domains like Medicaid (Healthcare), Claims and Providers Exposure to AI/ML model validation or testing data pipelines that support machine learning workflows. ISTQB or equivalent QA certification.
Familiarity with data warehouse concepts (star/snowflake schema, fact/dimension tables).
Strong analytical and problem-solving skills. Excellent communication and documentation abilities.
Ability to work independently and in a team environment. Strong understanding of data quality, data profiling, and reconciliation techniques. Experience in validating data pipelines and data transformations.
This includes but is not limited to: -
Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterprise.
Applied Productivity: Leverage AI tools to enhance coding, documentation, data analysis, and decision-making workflows.
Continuous Learning: Stay current with evolving AI capabilities and features and apply them to improve delivery quality and velocity.
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