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Vetted BigQuery Professionals
Pre-screened and vetted.
Junior Data Analyst & Business Analyst specializing in BI, analytics, and process optimization
Mid-Level Full-Stack Software Engineer specializing in cloud-native security & compliance platforms
Mid-level Full-Stack/AI Engineer specializing in LLM microservices, RAG, and data pipelines
Mid-level Data Scientist specializing in GenAI, MLOps, and computer vision for robotics
Mid-level Software Engineer specializing in cloud microservices and AI search
Intern-level Business Analytics professional specializing in data science and BI
Entry-Level Data Scientist specializing in machine learning, NLP, and cloud analytics
Mid-level Data Analyst specializing in BI, reporting automation, and operational analytics
Mid-level Generative AI Engineer specializing in LLMs, RAG, and MLOps
Mid-level Database Developer specializing in SQL, ETL, and cloud data platforms
Entry-Level Software Engineer specializing in healthcare data and AI-enabled tools
Senior Data Scientist / ML Engineer specializing in NLP, speech AI, and computer vision
Mid-Level Full-Stack Software Engineer specializing in cloud microservices and Voice AI
Mid-level Back-End Engineer specializing in scalable APIs and multi-tenant systems
Junior Software/AI Engineer specializing in LLM agents and RAG systems
kireeti Sangarsh
Mid-level Data Engineer specializing in cloud ELT pipelines and analytics engineering
“Data engineer who has owned end-to-end ELT pipelines on Airflow + AWS (S3/Glue/Lambda) with Snowflake/Redshift, processing millions of records per day and tens of GBs via PySpark. Built strong data quality and reliability practices (40% quality improvement, 99%+ uptime), and also designed a resilient web-scraping system with anti-bot defenses and schema-change versioning plus REST APIs for serving curated data.”
namratha sai
Junior Healthcare Data Analyst specializing in clinical data validation and EHR/claims analytics
“QA/supplier-performance focused candidate who uses defect and delivery data to spot recurring issues early, identify root causes tied to rushed timelines/high workload, and implement practical process changes (e.g., added validation steps and tightened defect definitions). Emphasizes clear, metric-backed communication to align internal stakeholders and suppliers, then monitors post-change results to confirm sustained improvement.”
Aditya Reddy Gunda
Intern AI/ML & Data Engineer specializing in deep learning, NLP, and cloud data pipelines
“AI/ML practitioner with production experience building a RAG-powered contextual customer support agent, optimizing for low latency using vector databases and smaller LLMs. Also deployed a fraud detection model on Kubernetes with auto-scaling for heavy transactional loads, and improved chatbot accuracy by 15% through metric-driven testing and evaluation. Partners with Marketing on personalization/recommendation initiatives with measurable outcomes tied to customer feedback.”
Mid-level DevOps Engineer specializing in cloud infrastructure and CI/CD automation
Junior Full-Stack Software Engineer specializing in cloud-native microservices
Senior Machine Learning Engineer specializing in NLP and production ML systems