Vetted Data Quality Professionals

Mark Kemp

Screened ReferencesStrong rec.

Director of Product Management specializing in FinTech and financial data platforms

Austin, TX15y exp

“Product leader in financial services who has built both customer-facing and internal AI-enabled products, including an award-nominated retirement plan platform that drove submissions from 40% to 98% in good order and unlocked a $10M revenue stream. Also built product process and team structure from the ground up at Consero, with experience applying AI/LLMs to workflow automation and reporting in complex, data-heavy environments.”

“Technical Product Manager with hands-on experience shipping and running live free-to-play games (including Cartoon Network BMX Champions) across web and mobile, with exposure to Roblox and some console. Focuses on data-driven live ops—daily rewards, streaks, push notifications, and limited-time events—paired with A/B testing and funnel/retention analytics to improve engagement and IAP performance.”

“Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.”

Mounika Nadendla

Screened ReferencesStrong rec.

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

5y exp

“Data engineer focused on building reliable, production-grade data systems end-to-end: batch and real-time pipelines (Airflow/Kafka/Spark) with strong data quality, monitoring/alerting, and incident response. Has experience integrating external API/web data with retries, throttling, and schema-change handling, and serving curated datasets to analytics (Power BI) and backend consumers with performance optimizations like Redis caching.”

“Built and operates a production LLM agent for enterprise IT operations that triages and drafts resolutions for high-volume ServiceNow tickets using LangChain + RAG (Pinecone/pgvector) and AWS Bedrock/OpenAI. Emphasizes reliability with schema-validated stages, offline eval datasets from real tickets, and CloudWatch-driven monitoring/guardrails; system scales to 40K+ tickets/month and cut resolution time ~28%.”

Trevor Peace

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in Healthcare IT

San Francisco, CA27y exp

“Founding/sole engineer who has built multiple healthcare products from scratch, spanning medical billing, cardiology triage, and cardiac device monitoring. Particularly compelling for healthtech and AI roles: they combine full-stack TypeScript/AWS execution with strong product judgment in regulated settings, including shipping voice-enabled RAG workflows and rejecting unsafe AI automation when simpler human-in-the-loop UX was the right answer.”

“Backend-focused engineer with healthcare and finance experience (Cardinal Health, JPMorgan) who has owned end-to-end data flows powering dashboards, emphasizing strong validation/data quality and measurable frontend performance gains. Has shipped Spring Boot REST APIs with versioning and Swagger docs, and has stood up an MVP task management system with GitHub Actions CI/CD, Docker, and AWS EC2 deployment.”

“Built a production RAG-based “unified assistant” that consolidates siloed company documents into a single chatbot while enforcing fine-grained access control via RBAC/metadata filtering with OAuth2/JWT. Experienced orchestrating LLM workflows with LangChain/LangGraph + FastAPI (async + caching) and measuring performance via retrieval accuracy and response-time SLAs. Also delivered a churn analytics solution with dashboards and automated retention campaigns using n8n.”

“Built and deployed a production RAG-based document Q&A system on Azure OpenAI to help business teams search thousands of PDFs/Word files, using Qdrant vector search, MongoDB, and a Flask API. Demonstrates strong production engineering (streaming large-file ingestion, parallel preprocessing, monitoring/retries) plus systematic prompt/embedding/chunking experimentation to improve accuracy and reduce hallucinations, and has hands-on orchestration experience with ADF/Airflow/Databricks/Synapse.”

“ML/NLP engineer who built a RAG-based technical assistant for Caterpillar field engineers, transforming PDF keyword search into intent-based semantic retrieval across manuals, logs, sensor reports, and technician notes. Strong in productionizing data/ML systems (Airflow, PySpark) with rigorous preprocessing, entity resolution, and evaluation—delivering measurable gains in accuracy, relevance, and duplicate reduction.”

“Built and deployed a production LLM system that scraped Google Maps menu photos, extracted structured prices via OpenAI, and cross-validated them against website-scraped data to automate data-quality verification at scale (replacing costly manual contractor checks). Demonstrates strong reliability instincts—precision-first prompting, output gating with image-quality metadata, and fuzzy matching/RAG techniques—plus solid orchestration (Dagster/Airflow) and observability (Sentry, Prometheus/Grafana).”

“Data engineer focused on high-volume transaction pipelines (2M+ per day) using Snowflake/Snowpipe, Spark/PySpark, Kafka, and Airflow, with a strong emphasis on schema/data-quality enforcement and reliability improvements. Also built a greenfield compliance-focused RAG solution, using CloudWatch monitoring and adding ingestion validation to prevent malformed OCR documents from degrading search quality.”

“Data engineer in healthcare (HCA) who owned end-to-end Azure-based pipelines at very large scale (50M+ daily claims/patient records). Strong focus on reliability: schema-drift fail-fast validation, quarantine layers, and Python/SQL data quality checks that reduced issues ~25%, plus performance tuning in Databricks/PySpark and versioned serving in Synapse for downstream consumers.”