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Vetted Azure Data Factory Professionals
Pre-screened and vetted.
Mid-level Data Analyst & ML Engineer specializing in GenAI, NLP, and cloud data pipelines
Senior Multi-Cloud DevOps/SRE Engineer specializing in Azure, AWS and IaC
Mid-level Data Engineer specializing in cloud data pipelines and analytics platforms
Mid-level Data Engineer specializing in streaming, lakehouse platforms, and LLM-driven data workflows
Mid-level Data Scientist specializing in customer analytics, ML pipelines, and churn forecasting
Mid-level AI/ML Engineer specializing in GenAI, RAG, and cloud-native ML platforms
Mid-Level Software Engineer specializing in cloud data platforms and full-stack web development
Senior Software & Data Engineer specializing in cloud platforms and streaming data
Mid-level Full-Stack Developer specializing in FinTech and fraud detection
Mid-level Data Scientist specializing in marketing analytics and scalable data platforms
VP Data Engineer specializing in AI-driven analytics platforms for investment management
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
Mid-level Data Engineer specializing in cloud lakehouse and streaming pipelines
Mid-level Data Engineer specializing in streaming and cloud lakehouse platforms
Mid-level Data Engineer specializing in cloud-native ETL and data warehousing
Senior AI Platform Engineer specializing in agentic AI and RAG systems
Murali Krishna Vempalli
Mid-level Data Analyst specializing in retention, churn, and customer analytics
“Analytics professional with experience across healthcare and fintech, including building SQL/Python data pipelines at Optum and owning a fraud detection initiative at Razorpay. Stands out for combining messy-data cleanup, reproducible analytics workflows, and stakeholder-driven metric design, with a reported 25% improvement in fraud detection while keeping false positives under control.”
David Kidwell
Senior AI/ML Data Scientist specializing in NLP, computer vision, and MLOps
“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”