Vetted Data Warehousing Professionals

“Customer-support/engineering background spanning Informatica PowerCenter ETL and IBM demos/workshops, with hands-on experience hardening data workflows for production (error tables/reject links, validation, restart strategies, alerting, performance tuning). Also demonstrates a clear, systems-level approach to diagnosing LLM/agentic workflow issues (prompt/RAG/tooling/memory) using instrumentation and iterative fixes, and has partnered with sales on POCs by defining success metrics and mapping solutions to customer architectures.”

“Founder of an LLC operating as a consulting firm providing fractional CTO services to startups, giving them parallel exposure to multiple early-stage companies. Has direct experience with MVP development, building org structures from scratch, and supporting early fundraising, and is exploring a pivot from consulting into a scalable product business while staying engaged with the VC/accelerator ecosystem.”

“Built a production AI-driven contract/document extraction system combining OCR, normalization, and LLM schema-guided extraction, orchestrated with PySpark and Azure Data Factory and loaded into PostgreSQL for analytics. Emphasizes reliability at scale—using strict JSON schemas, confidence scoring, targeted retries, and multi-layer validation to control hallucinations while processing thousands of PDFs per hour—and partners closely with non-technical business teams to refine fields and deliver usable dashboards.”

“Backend engineer with hands-on experience building secure Python/Flask services (sessions, JWT, RBAC) and optimizing PostgreSQL/SQLAlchemy performance, including custom SQL using CTEs/window functions profiled via EXPLAIN ANALYZE. Also integrates LLM features via OpenAI/Azure into backend systems and improves scalability with RabbitMQ-driven async processing, caching, and multi-tenant data isolation patterns.”

“AI/ML engineer focused on real-time, production-grade LLM systems, with a robotics-adjacent mindset around latency/accuracy tradeoffs and modular pipelines. Built a scalable RAG-based assistant orchestrated as microservices on Kubernetes with Kafka async messaging, ONNX/quantization optimizations, and monitoring (Prometheus/Grafana), citing a ~35% hallucination reduction; has also experimented with ROS Noetic/Gazebo to understand ROS concepts.”

“Engineering leader/player-coach who modernized a legacy C#/SQL Server system to Snowflake + Python on GCP, enabling ~30x scale and supporting hundreds of millions of transactions per day per customer. Strong in architecture tradeoffs (Snowflake vs Databricks), production reliability (New Relic, logging/alerting), and lightweight process improvements like a rigorous Definition of Done and structured PR reviews.”

“Healthcare data/ML practitioner with experience at UnitedHealth Group building production ETL and streaming pipelines (Python, BigQuery, Kafka) that unify EHR, IoT device, and lab data for patient risk prediction. Also implemented embedding-based semantic search/linking for noisy clinical notes via domain adaptation and rigorous validation with clinical stakeholders; previously built churn prediction at DirecTV using XGBoost.”

“Hands-on engineering manager/lead focused on Azure application deployments and CI/CD automation. Has led design sessions and 1:1 code reviews, and owned a production performance incident end-to-end from measurement and test-environment setup through production validation, while coaching team members via regular 1:1s.”

“Data/MLOps engineer (Cognizant background) who owned an AWS/Airflow/Snowflake healthcare transactions pipeline processing ~8–10M records/day and cut pipeline/data-quality incidents by ~33%. Also built and deployed a production FastAPI model-inference service on Kubernetes (Docker, HPA) with strong observability (Prometheus/Grafana), versioned endpoints, and resilient backfill/idempotent external data ingestion patterns.”

“Developer with experience across Drupal and Java/Spring Boot applications using React/jQuery for UI and API-driven features. Has handled production issues by tuning reverse proxy timeouts for login problems and troubleshooting data pipeline inaccuracies by fixing database queries, with a focus on performance and careful verification before changes.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“Data engineer with end-to-end ownership of large-scale retail and clinical data ingestion/processing on AWS, including real-time streaming and batch pipelines. Delivered measurable outcomes: 20M daily transactions processed, latency cut from 4 hours to 5 minutes, ~70% fewer failures, and 120+ pipelines running at 99.8% reliability with full audit compliance.”

“React performance-focused engineer who contributed performance patches back to an open-source context+reducer state helper after profiling and fixing excessive re-renders in an enterprise project management platform at Easley Dunn Productions. Also built an end-to-end LLM-driven pipeline at Prime Healthcare to normalize millions of supply-chain records, reducing defects by 80% and saving 160+ hours/month.”

“Designed and deployed an enterprise LLM-powered clinical/pharmacy policy knowledge assistant at CVS Health, replacing manual searches across PDFs/Word/SharePoint with a HIPAA-compliant RAG system. Built end-to-end ingestion and orchestration (Airflow + Azure ML/Data Lake + vector index) with PHI masking, versioned re-embedding, and production monitoring (Prometheus/Grafana), and partnered closely with clinicians/compliance to ensure policy-grounded, auditable answers.”

“At Dreamline AI, built and productionized an AWS-based incentive intelligence platform that uses Llama-2/GPT-4 to extract eligibility rules from unstructured state policy documents into structured JSON, then processes them with Glue/PySpark and serves results via Lambda/SageMaker/API Gateway. Designed state-specific ingestion connectors plus schema validation and automated checks/alerts to handle frequent policy/format changes without breaking the pipeline, and partnered with business/analytics stakeholders to deliver interpretable eligibility decisions via explanations and dashboards.”

“Backend/Python engineer who owned an end-to-end FastAPI + AWS internal natural-language document Q&A system (Textract extraction, embeddings/vector DB, LLM integration) with strong focus on reliability and latency. Hands-on with Kubernetes + GitOps (Argo CD, Helm, rolling updates/auto-rollback) and built/optimized Kafka streaming pipelines using Prometheus/Grafana. Also supported a zero-downtime on-prem to cloud migration with parallel run and gradual traffic cutover.”

“QA engineer intern experience at Amazon (Alexa Daily Essentials) owning end-to-end quality for AI-powered timer/stopwatch features at massive scale. Demonstrates disciplined Jira-based workflow, automation-driven regression coverage, and strong device-matrix verification (Echo Show generations), with concrete examples of finding and driving resolution of complex UI/backend synchronization bugs.”

“Backend engineer with experience at HSBC and Machinations who has delivered major production performance wins (cutting large trade-file upload times from ~13–15s to ~2s) using chunked parallel processing with strong reliability controls. Also built and shipped an applied AI RAG workflow using Langflow + Cohere embeddings + FAISS with hosted/local LLM fallbacks (Hugging Face, Ollama) and production-grade guardrails, observability, and evaluation.”

“AI engineer who shipped a production chat assistant for a storage company by building the underlying RAG-style knowledge base (document ingestion, chunking/embeddings, FAISS vector store) and an admin update interface to keep content current. Also has full-stack delivery experience (Python REST APIs + React/TypeScript UI) and AWS operations using Terraform/Jenkins, including handling a real production performance incident by optimizing DB queries and adding auto-scaling.”

“Full-stack engineer who has shipped and owned production analytics dashboards using Next.js App Router + TypeScript, combining server components for data-heavy pages with client components for interactive charts/filters. Also built a Temporal-orchestrated payment reconciliation workflow with versioning, idempotency, and exponential-backoff retries, and has hands-on Postgres query/index optimization using EXPLAIN ANALYZE.”

“Data engineer/backend data services practitioner with Bank of America experience building real-time and batch transaction-monitoring pipelines and APIs (Kafka + databases, REST/GraphQL). Highlights include a reported 45% response-time improvement through performance optimizations and use of Delta Lake schema evolution plus CI/CD (GitHub Actions/Jenkins) and operational reliability patterns like CloudWatch monitoring and dead-letter queues.”

“Applied NLP/ML engineer with experience at KeyBank and Novartis building production document intelligence and entity-resolution systems in finance and healthcare. Has delivered end-to-end pipelines (Airflow + AWS) using transformers (DistilBERT/Sentence-BERT), vector search (FAISS/Milvus/Pinecone), and human-in-the-loop labeling to achieve measurable gains (40%+ faster queries; up to 88% F1 and 93% precision/90% recall in entity linking).”

“Engineering leader focused on developer platforms and open-source frameworks/SDKs, with strong community and release-engineering chops. Drove major reliability and DX improvements (30–50% faster release cycles; 2–3x repeat contributors; ~50% faster onboarding) and led an incremental Python monolith to TypeScript event-driven migration using Protobuf contracts, feature flags, and a plugin architecture to preserve backward compatibility.”