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Vetted AWS Glue Professionals
Pre-screened and vetted.
Mid-level Data Engineer specializing in cloud data platforms and real-time analytics
Staff Machine Learning Engineer specializing in LLMs, recommendations, and MLOps
Principal AI Architect specializing in GenAI, agentic systems, and RAG
Mid-level Data Engineer specializing in cloud data platforms and real-time analytics
Senior Data & ML Engineer specializing in big data platforms and marketing/ads ML
Senior Full-Stack Engineer specializing in Python and AWS serverless systems
Senior Backend/Platform Engineer specializing in AWS-native data processing systems
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Senior Software Engineer specializing in AWS serverless backend and data engineering
Senior Full-Stack Python Developer specializing in cloud, data platforms, and GenAI
Mid-level Machine Learning Engineer specializing in fraud detection and recommendations
Staff AI & Data Engineer specializing in LLM systems and real-time data platforms
Mid-level AI/ML Engineer specializing in cloud MLOps and GenAI for fraud detection
Mid-level Full-Stack Software Engineer specializing in cloud-native and data platforms
Executive Engineering Leader (CTO/VP) specializing in platform scaling and video streaming
Pavanika Thotakura
Senior Data Engineer specializing in cloud big data pipelines and real-time streaming
“Amazon data engineer who built a real-time fraud detection pipeline for AWS Lambda, tackling multi-region telemetry quality issues and scaling stream processing for billions of daily requests. Strong in production-grade data/ML workflows on AWS (EMR, Glue, Kinesis, SageMaker) with hands-on entity resolution and anomaly detection.”
Sara Fang
Mid-level Software Engineer specializing in cloud data platforms and distributed systems
“Backend/data engineer with production experience building FastAPI services with strong reliability patterns (circuit breaker, rate limiting, caching, graceful degradation) and JWT/OAuth2 auth. Has delivered AWS EKS deployments via Terraform with Secrets Manager/IRSA and HPA autoscaling, and built Glue/Spark ETL pipelines on S3 Parquet with schema-evolution and idempotent reruns; also demonstrated measurable SQL tuning impact (20–30s to <10s).”