Vetted Power BI Professionals

Sai Rahul Dasari

Screened

Mid-level AI/ML Engineer specializing in Generative AI, NLP, and healthcare RAG systems

USA3y exp

“Built and deployed a production clinical claim validation RAG system at GE HealthCare that automated nurses’ patient-history/claims checks, cutting manual review time by ~65%. Designed the full stack (retrieval, embeddings, Pinecone, prompt/verification guardrails, FastAPI backend) with PHI-compliant anonymization via NER and orchestrated pipelines using Airflow, Azure ML Pipelines, and MLflow with drift monitoring.”

Surya Vamshi Sriperambudooru

Screened

Mid-level AI Engineer specializing in healthcare claims analytics and RAG copilots

Remote, US4y exp

“Built a production "appeals co-pilot" for a healthcare claims appeals team, combining an XGBoost/logistic ranking model with a Python/LangChain RAG stack (FAISS + Mistral 7B) to surface high-probability appeal wins and speed policy-grounded drafting. Emphasizes reliability and trust: hybrid retrieval with metadata routing, citation/eval scripts, guardrails, and an explainability layer that non-technical stakeholders could understand and override.”

Prasanth Sai

Screened

Mid-level Data Engineer specializing in cloud lakehouse/warehouse pipelines

4y exp

“Data engineer with HCA Healthcare experience building and operating end-to-end AWS-based pipelines for clinical and operational reporting (50–100 GB/day), serving curated data into Redshift/Snowflake for Power BI/Tableau. Emphasizes production reliability (Airflow SLAs/retries/alerting, logging/observability) and strong data quality controls (reconciliations, schema/null/duplicate checks), and has shipped versioned REST APIs to expose warehouse data to downstream systems.”

“Accenture data engineer who greenfielded a supply-chain lakehouse platform, building an end-to-end medallion/Delta pipeline ingesting ~1.4TB/day from 17+ ERP/WMS/TMS/shipment sources. Delivered Gold datasets to Redshift/Synapse/Databricks SQL powering Power BI/Tableau with a 99.5% SLA, while cutting runtime 30% and cloud costs 16% through Spark/Delta optimizations and robust data quality controls.”

Shashank R

Screened

Senior Data Engineer specializing in cloud data platforms and real-time analytics

Las Vegas, NV6y exp

“Data engineer (Credit One) who built and owned real-time financial transaction and credit risk/fraud data systems end-to-end on AWS + Snowflake. Delivered high-scale pipelines (150k events/hour; ~2TB/week), raised data accuracy to 99%, and cut Snowflake costs 42% while adding strong observability, schema-drift handling, and production-grade APIs/documentation.”

Ashrita Mishra

Screened

Mid-level Data Analyst specializing in analytics, ETL, and cloud data platforms

Jersey City, NJ4y exp

“Data analyst with 4 years of experience spanning banking and retail/marketing analytics. Has hands-on experience building churn analytics pipelines in SQL and Python, optimizing large-query performance, and turning stakeholder-aligned metrics into recurring dashboards and business actions.”

“Analytics professional with hands-on experience building SQL/Python pipelines, customer ID mapping logic, and self-serve BI dashboards across marketing/CRM and regulated aviation reporting environments. Particularly strong in turning messy multi-source data into trusted reporting assets, with repeated claims of major efficiency gains, faster decision-making, and high-confidence stakeholder adoption.”

Mehar Matta

Screened

Junior Marketing Analytics professional specializing in B2B SaaS and AI-driven insights

Richardson, TX2y exp

“Analytics professional with hands-on experience building SQL and Python workflows for marketing, funnel, and revenue reporting across tools like Salesforce, Google Analytics, and Power BI. They stand out for turning messy, multi-source data into trusted reporting layers, aligning sales and marketing on shared KPI definitions, and using segmentation/cohort analysis to improve campaign targeting and conversion performance.”

“Recent college graduate with early outbound experience as a student caller, where they stood out as a top performer by raising the highest donation amounts on many shifts. Their edge is a highly personal, relationship-first approach—moving beyond scripts to connect alumni memories and motivations to a compelling ask.”

“ML/AI engineer with hands-on experience building production MLOps systems for predictive maintenance and demand forecasting, including deployment, monitoring, and iterative retraining. Also shipped a RAG-based employee onboarding chatbot integrated with ServiceNow APIs and reports business impact of roughly $300k/month in reduced stockout and overstock costs.”

Meet Vyas

Screened

Mid-level Software Engineer specializing in full-stack systems and AI applications

Tempe, AZ3y exp

“Software engineer with 3 years at Gap Inc. who led a major modernization from monolith/legacy frontend to a React and Spring Boot microservices architecture, delivering 25% cost savings, 30% faster releases, and 50% performance gains. Also built a 0→1 startup product, MealShare AI, using managed cloud services to rapidly launch a real-time food redistribution platform.”

“Built a prediction market analytics website from scratch to analyze volume and correlations in decentralized markets, drawing on a sports and finance background. Shows strong early-stage product instincts, backend ownership, and a pragmatic approach to scalability, security, and AI-assisted data workflows.”

Lokesh Jain

Screened

Senior AI/ML Engineer specializing in supply chain and healthcare systems

Bentonville, AR6y exp

“Built and deployed AcademiQ Ai, a production LLM-based teaching assistant using GPT/BERT with RAG (LangChain + Pinecone) to handle large student notes and generate adaptive explanations/quizzes. Demonstrated measurable retrieval-quality gains (18% precision improvement, 22% less irrelevant context) by tuning similarity thresholds and chunking based on user satisfaction signals. Also orchestrated terabyte-scale, real-time demand forecasting pipelines using Airflow and Kubeflow on GCP with strong monitoring, shadow deployment, and feedback-loop practices.”

“Built and shipped enterprise AI products including a conversational SQL analytics platform and a production RAG system at Johnson & Johnson. Combines full-stack engineering with LLM systems expertise, and has delivered measurable impact at scale, including 48% lower retrieval latency and 37% better response relevance across 12M+ records.”

“Backend/full-stack engineer who has owned production retail and order/inventory systems end-to-end, using Spring Boot microservices with Kafka event-driven workflows. Strong in production correctness patterns (idempotency, retries/DLQs, schema versioning) plus observability (Prometheus/Grafana) and developer-facing API design (Swagger, OAuth2/JWT, versioning/deprecation). Also built TypeScript/React SPAs and cited ~40% UI performance improvement.”