Overview
On Site
DOE
Contract - W2
Skills
FOCUS
Attention To Detail
Bridging
Data Engineering
Data Integrity
Functional Requirements
Test Cases
Automated Testing
Extract
Transform
Load
Collaboration
Software Development Methodology
Testing
Quality Assurance
GUI QA
Workflow
Communication
Quick Learner
Microsoft Azure
Continuous Integration
Continuous Delivery
YAML
Databricks
Artificial Intelligence
Machine Learning (ML)
SQL
Python
Scripting
PostgreSQL
Stored Procedures
Data Validation
Snow Flake Schema
Data Quality
NeoLoad
Tosca
Selenium
HP
UFT
Job Details
Job Summary We are seeking a detail-oriented and technically proficient QA Analyst to support a machine learning (ML) platform focused on full-stack and data-centric applications. This role bridges automation, data engineering, and ML operations, ensuring robust testing of production-grade applications and data pipelines. The ideal candidate will be business-savvy, collaborative, and capable of validating data integrity and application functionality across teams. Key Responsibilities Lead and execute QA testing across full-stack and data/ML applications. Translate business and functional requirements into test cases and validation strategies. Perform manual and automated testing, including ETL and source-to-target data validation. Collaborate with data engineers, data scientists, and QA teams throughout the SDLC. Validate SQL queries and Python scripts to ensure data accuracy and business logic alignment. Support QA efforts for applications transitioning from development to production. Recommend and implement appropriate testing tools and frameworks. Ensure data quality, consistency, and governance across all QA deliverables. Required Qualifications 23 years of QA experience in a production environment. Experience in data-centric QA roles (beyond front-end testing). Familiarity with ML workflows and data pipelines. Strong communication skills and ability to work independently. Quick learner with a proactive mindset. Primary Skills (Ranked in Order) Azure CI/CD Automation YAML Selenium QTest Databricks Complex SQL Queries Python SonarQube CodeQL Integration with AI/ML Applications Technical Skills & Tools SQL (Advanced): Ability to write and validate complex queries. Python (Intermediate): Comfortable reading, understanding, and modifying scripts. PostgreSQL: Experience with stored procedures, triggers, and data validation. Snowflake: Familiarity with querying and validating data. Testing Tools: Experience with one or more of the following: QTest NeoLoad Tosca Katalon Selenium HP UFT Education: Bachelors Degree
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