Data Testing Cloud Consultant

Job Overview:

• We are in search of a Quality Engineering Lead to spearhead the execution of AI Data Assurance initiatives, focusing on the establishment of reliable, high-quality, and AI-ready data foundations.
• This position entails crafting quality strategies, instituting AI Data Assurance frameworks, promoting automation, and ensuring the integrity of AI-ready data that aligns with trustworthy, responsible, and business-oriented AI outcomes.

Primary Responsibilities:

• Leadership in Project Delivery
• Oversee the comprehensive execution of AI Data Assurance projects
• Facilitate delivery governance, quality metrics, executive reporting, and excellence in Agile Hybrid delivery
• Governance in Quality Engineering AI Assurance
• Establish quality strategies, testing frameworks, and assurance processes for AI, ML, GenAI, AI data assurance, analytics, and BI platforms
• Manage the end-to-end validation process, release readiness, and quality gates
• Supervise testing and validation of data platforms, pipelines, analytics solutions, BI platforms, and AI-ready datasets
• Implement AI Data Harness Assurance across data pipelines, RAG systems, vector stores, and AI workflows
• Enhance AI Data Outcome Assurance through assessment of AI output quality, reliability, explainability, and business alignment
• Support initiatives related to Responsible AI, AI Governance, and Model Assurance
• Automation, Client Focus, and Team Leadership
• Develop automation frameworks for AI Data Assurance, BI assurance, and ongoing quality monitoring
• Integrate quality controls and assurance gates within DataOps, MLOps, and CI/CD pipelines
• Guide and mentor AI Data Assurance teams, fostering capability development, quality reviews, and continuous improvement
• Collaborate with business, product, data engineering, architecture, AI, ML, and platform teams to achieve AI transformation goals
• Drive initiatives for automation, AI-assisted testing capability development, and continuous improvement
• Create AI data assurance accelerators and engage in client demonstrations
• Contribute to client pursuits, solution proposals, estimations, and AI assurance offerings
• Forge partnerships, develop thought leadership assets, innovation frameworks, webinars, workshops, and knowledge-sharing initiatives

Technical Qualifications:

• Essential Skills and Experience
• Minimum of 5 years in Data Quality Engineering, Analytics Testing, or Data-driven transformation programs
• At least 3 years of experience leading AI Data Assurance, AI GenAI Analytics, or AI Quality Engineering projects
• Comprehensive knowledge of AI, ML, GenAI, LLMs, various RAG architectures, Prompt Engineering, Vector Databases, DataOps, MLOps, and AI Governance
• Strong proficiency in ETL Testing, Analytics, BI Testing, Reporting Validation, AI Data Readiness Assurance, AI Data Harness Assurance, AI Data Outcome Assurance, and Continuous AI Assurance
• Practical experience with Cloud Data AI platforms such as Azure, AWS, GCP, Databricks, Snowflake, Microsoft Fabric, or comparable technologies
• Exceptional leadership, stakeholder management, communication, and mentoring capabilities
• Location will be determined based on business needs

Additional Responsibilities:

• Technical and Professional Requirements
• Agile Delivery and Quality Governance
• Expertise in AI Data Assurance, AI, ML, GenAI, LLMs, and RAG Architectures
• Competence in Data Quality, Data Governance, and Responsible AI
• Experience with ETL, Data Warehouse, Analytics, BI, and Data Integration Testing
• Proficiency in SQL, Snowflake, Databricks, Informatica, Azure Data Factory (ADF)
• Knowledge of Prompt Engineering and Retrieval Assurance
• Familiarity with Python, PySpark, and Test Automation
• Experience with Playwright and API Testing
• Understanding of Vector Databases, AI Data Pipelines, DataOps, and MLOps
• Knowledge of Azure, AWS, GCP Data AI Platforms
• Familiarity with Jira, Zephyr, Azure DevOps, and CI/CD processes

Preferred Skills:

• Technology Expertise: AI - Generative AI
• Data Services Testing: Big Data Testing