Data Testing Cloud Consultant

Required Skills & Experience

• 5+ years of experience in Data Quality Engineering, Analytics Testing, or Data driven transformation programs.
• 3+ years leading AI Data Assurance, AI/GenAI, Analytics, or AI Quality Engineering initiatives
• Strong knowledge of AI/ML, GenAI, LLMs, various RAG Architectures, Prompt Engineering, Vector Databases, DataOps/MLOps, and AI Governance.
• Strong expertise in ETL Testing, Analytics & BI Testing, Reporting Validation, AI Data Readiness Assurance, AI Data Harness Assurance, AI Data Outcome Assurance and Continuous AI Assurance
• Hands-on Experience with Cloud Data & AI Platforms such as Azure, AWS, GCP, Databricks, Snowflake, Microsoft Fabric, or similar.
• Strong leadership, stakeholder management, communication, and mentoring skills "Location will be based on business requirement." Project & Delivery Leadership
• Lead end-to-end delivery of AI Data Assurance programs.
• Drive delivery governance, quality metrics, executive reporting, and Agile/Hybrid delivery excellence. Quality Engineering, AI Assurance & Governance
• Define quality strategies, testing frameworks, and assurance processes for AI/ML, GenAI, AI data assurance, analytics, and BI platforms.
• Govern end-to-end validation, release readiness, and quality gates.
• Lead 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.
• Drive AI Data Outcome Assurance by evaluating AI output quality, reliability, explainability, and business alignment.
• Support Responsible AI, AI Governance, and Model Assurance initiatives. Automation, Client Orientation & Team Leadership
• Build automation frameworks for AI Data Assurance, BI assurance and continuous quality monitoring.
• Embed quality controls and assurance gates within DataOps, MLOps, and CI/CD pipelines.
• Lead and mentor AI Data Assurance teams and drive capability development, quality reviews, and continuous improvement.
• Collaborate with business, product, data engineering, architecture, AI/ML, and platform teams to deliver AI transformation initiatives.
• Drive automation, AI assisted testing, capability development, and continuous improvement initiatives.
• Build AI data assurance accelerators and participate in client demos
• Contribute to client pursuits, solutioning, proposals, estimations, and AI assurance offerings.
• Build partnerships, thought leadership assets, innovation frameworks, webinars, workshops, and knowledge-sharing initiatives. Technical & Professional Requirements
• Agile Delivery, Quality Governance
• AI Data Assurance, AI/ML, GenAI, LLMs & RAG Architectures
• Data Quality, Data Governance & Responsible AI
• ETL, Data Warehouse, Analytics, BI & Data Integration Testing
• SQL, Snowflake, Databricks, Informatica & Azure Data Factory (ADF)
• Prompt Engineering & Retrieval Assurance
• Python, PySpark & Test Automation
• Playwright, API Testing
• Vector Databases, AI Data Pipelines, DataOps & MLOps
• Azure, AWS & GCP Data & AI Platforms
• Jira, Zephyr, Azure DevOps & CI/CD