Vetted Snowflake Professionals

“Long-tenured EdTech operator who joined LinkIt! as the third Data Analyst when the company had fewer than 40 employees and grew into a Data Quality Manager over 7+ years. Stands out for blending data operations leadership—ETL migration, reporting automation, process documentation, and training—with highly hands-on startup execution across office operations, hybrid work rollout, and crisis response during COVID.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”

“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

“AI/ML Engineer with 4+ years of experience (Capital One, Odin Technologies) and a master’s in Data Analytics (4.0 GPA) who has deployed LLM/RAG systems to production for compliance/risk and document review. Strong in orchestration and MLOps (Airflow, Kubernetes, MLflow, GitHub Actions) and in tackling real-world LLM constraints like latency, context limits, and data privacy, with measurable impact (20%+ manual review reduction; 33% faster release cycles).”

“Customer-facing data engineering professional who builds and deploys real-time reporting/dashboard solutions, gathering reporting and compliance requirements through direct stakeholder engagement. Experienced with Google Cloud IAM governance, secure integrations (encryption, audit logging), and fast production troubleshooting of ETL/pipeline failures with follow-on monitoring and automated recovery improvements; motivated by hands-on, travel-oriented customer work.”

“Full-stack/backend engineer with deep insurance claims domain experience who built and operated a microservices + ETL platform (Java/Spring Boot + Python + Kafka/Databricks) processing 1M+ daily transactions. Combines production-grade reliability (99.7% uptime, zero-downtime blue/green releases, strong observability) with customer-facing UI delivery (AngularJS/React+TS dashboards and a hackathon-winning research chatbot).”

maheen Adeeb

Screened

Senior Machine Learning Engineer specializing in LLMs, speech AI, and RAG systems

Chicago, IL3y exp

“AI engineer with production experience building multilingual speech-to-speech translation pipelines (ASR + LLM) for enterprise/media, focused on reliability at scale. Has hands-on orchestration experience (including IBM Watson contexts) and emphasizes production evaluation/monitoring using a mix of traditional metrics and LLM-based evaluators to catch quality regressions while balancing latency and cost.”

Brian Mar

Screened

Senior Data Engineer specializing in data infrastructure and marketing/CRM analytics

San Mateo, CA8y exp

“Salesforce-focused implementation/solutions engineer from Full Circle Insights who owned end-to-end campaign attribution and reporting deployments for multiple customers at once (3–5 concurrently), including sandbox testing, KPI monitoring, and rollback-safe migrations from legacy reporting. Also builds personal multi-agent workflows and uses Claude Code to rapidly scaffold data/analytics scripts like an advertising optimization parser over CSV/XLSX inputs.”

“Analytics professional with Northern Trust experience focused on investment portfolio reconciliation and reporting. They combine SQL, Python, and Power BI to clean and validate high-volume financial data, automate manual processes, and align operations and accounting teams on shared metrics—driving roughly 20% improvement in reconciliation accuracy.”

“Analytics professional at Optum with hands-on experience turning messy healthcare claims data from SQL, Excel, and CRM systems into validated reporting datasets and Power BI dashboards. They also built reproducible Python workflows for claims analysis and owned an end-to-end project focused on improving claims processing efficiency through metric design, segmentation, and stakeholder-driven operational improvements.”

“Worked on a P&C insurance integration project at Accenture, using SQL to unify policy, billing, and claims data from APIs and ETL pipelines into clean reporting tables. Demonstrated hands-on experience with data quality validation, window-function-based transformations, and query performance tuning, helping business teams get a single reliable view for faster claims processing and management reporting.”

“Analytics professional with hands-on experience unifying marketing-platform data through Fivetran and Snowflake, building reporting views, and catching source-to-report issues like timezone-driven spend discrepancies. They also owned subscription LTV/cohort analysis and engagement tracking initiatives, partnering with e-commerce, product, and senior leadership to turn behavioral and demographic data into dashboards, lead-qualification metrics, and lifecycle marketing insights.”

“Analytics professional from Capital One with strong experience automating risk, reconciliation, and regulatory reporting workflows in financial services. They combine deep SQL/Python pipeline skills with stakeholder-facing dashboard and KPI design, delivering measurable impact like 30% performance gains, sub-24-hour anomaly detection, and 100% data integrity for regulatory filings.”

“GenAI/ML engineer with hands-on experience building production financial intelligence and document summarization systems at Citibank. Stands out for combining LLM fine-tuning, hybrid RAG, multi-agent workflows, and strong MLOps/observability practices to deliver measurable business impact, including 60% faster analyst retrieval, 31% higher precision, and 99%+ uptime.”

“Analytics candidate with strong finance/operations reporting experience, focused on billing, margin, and revenue leakage use cases. They have built SQL and Python workflows that turned messy shipment, invoice, PO, and ERP data into trusted reporting layers, replacing manual Excel processes and driving adoption through transparent reconciliation and stakeholder collaboration.”

“Healthcare-focused LLM engineer who deployed a production triage and clinical knowledge retrieval assistant using RAG and LangGraph-orchestrated multi-agent workflows. Emphasizes clinical safety and compliance with robust hallucination controls, HIPAA/PHI protections (tokenization, encryption, audit logging, zero-retention), and human-in-the-loop escalation; reports a 75% latency reduction in a healthcare agent system.”

“Telecom (Verizon) AI/ML practitioner who built a production multimodal system that ingests messy customer issue reports (calls, chats, emails, screenshots, videos) and turns them into confidence-scored incident summaries with reproducible steps and evidence links. Also built KPI/alarm-to-ticket correlation to rank likely root-cause domains (RAN/Core/Transport), cutting triage from hours to minutes and improving MTTR.”

“Full Stack Developer (recently at Cisco Systems) building end-to-end web applications with Angular frontends and Spring Boot microservices backed by MySQL/JPA, including JWT + role-based access. Has hands-on experience with high-volume, real-time data processing/visualization and has solved complex UI state consistency issues using RxJS BehaviorSubjects; also applies layered state patterns in React with Redux Toolkit and uses AI dev tools (Cursor/Claude) strategically.”

“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”

“IBM engineer who built and deployed a production RAG-based LLM assistant using LangChain/FAISS with a fine-tuned LLaMA model, served via FastAPI microservices on Kubernetes, achieving 99%+ uptime. Demonstrates strong practical expertise in reducing hallucinations (semantic chunking + metadata-driven retrieval) and managing latency, plus mature MLOps practices (Airflow/dbt pipelines, MLflow tracking, monitoring, A/B and shadow deployments) and effective collaboration with non-technical stakeholders.”

“Built a production multimodal LLM-based vendor risk assessment platform that ingests SOC reports and other documents, uses a strict RAG pipeline with grounded evidence (page/paragraph citations), and dramatically reduces analyst review time. Experienced with LangGraph/LangChain/AutoGen for stateful, fault-tolerant agent workflows, and emphasizes reliability (schema validation, guardrails) plus low-latency delivery (~1–2s) through hybrid retrieval, reranking, caching, and model tiering.”

“Consulting background (Accenture) delivering technically complex solutions involving on-chain data and strict government security standards, including building isolated sandbox environments to move from PoC to production. Experienced in debugging agentic/LLM-style workflows (e.g., document scanning issues) with deterministic guardrails, preprocessing, and strong logging/monitoring. Has led large-scale crypto wallet workshops (including for the CFTC) and helped win business via clear, layered technical demos; also built internal marketing taxonomy tooling and drove adoption through cross-functional alignment.”