Vetted ETL Professionals

“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”

“Backend-focused engineer with deep Java/Spring expertise in fintech and SaaS integrations, including high-scale financial data pipelines and partner-facing APIs. Most notably re-platformed a 100M+ record ETL system to a custom concurrent Spring Batch architecture that cut failures dramatically and reduced infrastructure costs by over 90%, while also leading enterprise-grade event-driven integrations for customers like Bosch and Amazon.”

“Full-stack engineer with end-to-end ownership across React/TypeScript frontends, Spring Boot/Node microservices, and production ops on Docker/Kubernetes and AWS (ECS/CloudWatch). Built real-time healthcare eligibility and analytics systems at Cigna and an early-stage seller onboarding platform at Flipkart, driving measurable performance gains (35–40% latency/throughput improvements) through event-driven Kafka pipelines, Redis caching, and strong reliability/observability practices.”

“Operator/program leader with experience building operating models and execution cadences for data/analytics and energy data services across Entech, HealthEquity, and RealPage. Has launched an undefined energy data management service into regulated enterprise environments and driven cross-functional alignment via dashboards, SLAs, and OKRs—contributing to enterprise wins and a successful company sale.”

“Developed and productionized an AI-driven "indication finding" system for AbbVie to identify additional diseases a drug could target, working closely with clinical research teams on cohort inclusion/exclusion criteria and disease rollups. Leveraged an LLM to map clinical inputs to ICD codes and built configuration-driven ML pipelines (Cloudera ML, YAML, scheduled jobs) with structured testing and evaluation for reliability.”

“LLM/agentic workflow builder focused on productionizing document-processing systems. Redesigned pipelines with LangGraph + RAG, schema-aware validation, and eval/monitoring loops; known for fast incident diagnosis (restored accuracy from ~70% to >95% same day). Partners closely with sales and stakeholders to deliver tailored demos and drive adoption (reported +40%).”

“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”