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Vetted Data Warehousing Professionals
Pre-screened and vetted.
Prudhvi Avula
Senior Full-Stack Software Engineer specializing in Java, React, and distributed systems
“Implementation and AI workflow engineer who has owned customer deployments end-to-end for messaging/integration platforms and also built production-oriented LLM systems. Stands out for combining stakeholder-facing delivery leadership with hands-on experience in Kafka/MongoDB integrations, RAG/agent architectures, and resilient document-processing pipelines with strong validation and fallback controls.”
Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines
Senior Product Manager / Project Manager specializing in data platforms, BI, and cloud transformation
Mid-level Data Engineer specializing in cloud data pipelines and streaming analytics
Mid-level Machine Learning Engineer specializing in healthcare and enterprise analytics
Mid-level Data Engineer specializing in AI/ML, streaming, and lakehouse architectures
Senior Business Analyst / QA Lead specializing in cloud, security, and enterprise testing
Senior Software Engineer / DevOps specializing in cloud-native distributed systems
Senior Data Engineer specializing in AWS-based data pipelines and multi-tenant SaaS
Director of Architecture & Data Engineering specializing in enterprise data platforms
bharath burgoju
Mid-Level Data Engineer specializing in cloud data pipelines and big data platforms
“Data engineer with ~4 years of experience building Python-based data ingestion/processing services and real-time streaming pipelines (Kafka/PubSub + Spark Structured Streaming). Has deployed containerized data applications on Kubernetes with GitLab CI/Jenkins pipelines and applied GitOps to cut deployment time ~40% while reducing config drift. Also supported a legacy on-prem data warehouse/backend migration to GCP using phased migration and parallel validation to meet strict reliability/SLA needs.”
Mid-level Full-Stack Developer specializing in Java/Spring Boot, React/Angular, and cloud microservices
Director-level Product & Data Platform Leader specializing in AI, cloud data, and enterprise governance
Mid-level Data Engineer specializing in FinTech and AI-ready data platforms
Senior Data Scientist and Machine Learning Researcher specializing in NLP, LLMs, and MLOps
Senior QA Automation Engineer (SDET) specializing in Python, Playwright, and API testing
Executive Technology Leader specializing in cloud architecture, integration platforms, and DevOps
Junior Data Engineer specializing in cloud ETL/ELT and lakehouse platforms
Tanweer Ashif
Mid-level AI/ML Engineer and Data Scientist specializing in LLMs and MLOps
“Data science/AI intern at University at Buffalo Business Services who built and deployed production systems spanning classic ML and LLM assistants. Delivered real-time competitor intelligence for a Cornell-partnered, $1B beverage launch by scraping/cleaning 5,000+ SKUs and deploying models via API, then built a domain-aware LLM assistant to modernize Excel-based workflows with strong grounding, privacy controls, and sub-5s latency.”
Linda DN
Senior Cloud DevOps Engineer specializing in AWS architecture, IaC, and DevSecOps
“DevSecOps/AWS infrastructure engineer at Madison Logic who owns a 15-account AWS footprint and treats nearly all AWS resources as code (Terraform/CloudFormation). Led a CI/CD platform migration (Bitbucket → GitLab + GitHub Actions) supporting WordPress and containerized microservices, improving release frequency to weekly/daily, and has hands-on production incident response experience on ECS Fargate using Datadog with fast rollback via immutable ECR tags and task definition revisions.”
Ketan Kshirsagar
Mid-level Business Analyst specializing in data analytics and BI
“Healthcare analytics professional with hands-on experience turning messy claims, eligibility, and utilization data into validated BI-ready models using SQL and Python. They combine strong data engineering and KPI design skills with stakeholder-facing delivery, including Power BI prototyping, retention metric operationalization, and analyses that supported care management interventions and cost-control decisions.”