Vetted Tableau Professionals

“Built a production lead-generation system using AI agents that researches the internet for relevant leads and integrates RAG-based contact enrichment/shortlisting aligned to existing CRM data, enabling sales reps to focus more on selling. Also has hands-on AWS data orchestration experience (Glue, Step Functions) moving raw data into Redshift and evaluates agent performance with human-in-the-loop plus BLEU/perplexity metrics.”

“Full-stack engineer with experience at Western Union and Aptly (for Microsoft), building production systems spanning React/TypeScript frontends and .NET Core/microservices backends. Has delivered an engineer-facing diagnostics/configuration console with TanStack Query caching/background refresh and has hands-on experience hardening transaction-processing workflows with Kafka, Azure Functions, and Resilience4j, plus Postgres modeling and query optimization.”

“Backend engineer who evolved Coca-Cola bottlers' Trade Promotion Optimization platform at Coke One North America, building domain-focused microservices in Node.js and Python (Flask/FastAPI) with PostgreSQL. Experienced in multi-tenant security (OAuth2/JWT, RBAC, row-level scoping by bottler/region), API contract/versioning discipline, and Azure DevOps-driven incremental rollouts with strong observability.”

“Cloud-focused application security practitioner with hands-on AWS and Kubernetes experience, including securing a manufacturing monitoring platform (API auth, least-privilege IAM, CI/CD security checks) and troubleshooting a production data-ingestion outage caused by an overly restrictive IAM change. Experienced in implementing cloud-native security tooling (IAM Access Analyzer, Inspector, CloudWatch) and deploying monitoring/security agents via Kubernetes sidecars with Helm, Prometheus/Grafana, and Jenkins-driven CI/CD.”

“Full-stack/ML-leaning engineer who has shipped production-grade real-time analytics and an internal AI support assistant using RAG over enterprise documentation. Demonstrates strong systems thinking across scalability, reliability, observability, and LLM safety/evaluation (thresholded retrieval, RBAC, response validation, regression-gated evals), with concrete iteration based on performance metrics and user feedback.”

“Performance marketer focused on high-intent lead generation, with experience managing $80K+/month spend for a home services lead-gen business (bathroom/windows/roofing). Runs an intent-based full-funnel system across Google Search/LSA, Meta prospecting + retargeting, and selective Performance Max, using bottleneck-driven, statistically disciplined testing to scale while protecting lead quality.”

“Backend/data engineer with hands-on production experience across Azure and AWS: built FastAPI + PostgreSQL services with Azure AD OAuth2/JWT auth and strong reliability patterns (timeouts, retries, correlation IDs). Delivered AWS Lambda/ECS solutions with Terraform/CI-CD and cost controls (SQS buffering, reserved concurrency), and built/operated AWS Glue ETL pipelines into Redshift while modernizing legacy SAS reporting into Python microservices with parity testing.”

“Built and deployed production NLP/LLM systems including a multilingual (5-language) health misinformation detection pipeline with latency optimization (batching/quantization/caching) and explainability (gradient-based attention visualizations). Experienced orchestrating end-to-end AI workflows with Airflow and Prefect, and partnering with customer support ops to deliver an AI agent for ticket summarization and priority classification with clear, measurable acceptance criteria.”

“B2B growth and partnerships operator with experience at Convera leading GTM for a cross-border payments offering and driving a ~17% lift in qualified leads through refined positioning and multi-channel activation. Has run data-driven funnel experiments (Dentacloud) and built association-based co-marketing partnerships measured through both top-of-funnel engagement and downstream pipeline influence.”

“Full-stack engineer who built an AI-based inventory/procurement query system at Botlily/Botlerly using Flask and Google Sheets as a live knowledge base, overcoming Sheets latency with caching and structured in-memory models. Demonstrated strong LLM product engineering (40% accuracy improvement via preprocessing/prompting) and customer-driven iteration with bar/restaurant owners, evolving the tool into a more comprehensive inventory management and forecasting solution.”

“Lifecycle/CRM marketer at Datazoic who builds post-onboarding activation and retention programs end-to-end, partnering with Analytics for behavioral segmentation and running rigorous A/B tests across messaging, timing, and CTAs. Also orchestrates cross-functional product feature rollouts (Product/Creative/Analytics) to align customer messaging with in-product experience, driving high engagement and fewer customer inquiries.”

“At Provenbase, built and shipped a production LLM-powered semantic search and candidate matching platform (RAG with GPT-4/Gemini, multi-agent orchestration, Elasticsearch vector search) to scale sourcing across 10M+ candidate records and 1000+ data sources. Drove sub-second performance, cut LLM spend 30% with routing/caching, and improved recruiting outcomes (+45% sourcing accuracy; +38% visibility of underrepresented talent) through bias-aware ranking and tight collaboration with recruiting stakeholders.”

“Founder and financial-operations-focused consultant who builds FP&A/BI frameworks for early-stage and legacy businesses, including KPI systems tied to month-end close and integrated manufacturing/distribution datasets (BOM, PPV, rolling forecasts). Drove a 400% cash-position increase for a distribution client by redesigning collections analytics (customer-level DSO + proprietary PTC ratio) and has coached PE-installed executives through U.S. operational/legal and cultural nuances.”

“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”