Vetted Tableau Professionals

“Founder of an algorithmic trading startup who reports raising $25M+ over roughly the last three years. Has spent several years working closely with VC funds, focusing on fundraising and lead generation with VC/PE firms, and is strongly committed to entrepreneurship and scaling new technologies.”

“Enterprise CSM with martech/market-intelligence background (Pulse and Gartner context) who owns accounts end-to-end from onboarding through renewal and expansion. Known for executive-level value narratives (e.g., CPO using benchmarks in a board deck), multi-threading across Product and Legal, and using usage/segmentation analytics plus activation tactics (A/B testing, targeted messaging) to drive adoption and renewals.”

“Full-stack engineer who built and owned an AI-assisted job-matching dashboard in Next.js App Router/TypeScript, keeping LLM logic server-side and improving performance via deduplication, caching/revalidation, and streaming (35% fewer duplicate LLM calls; 40% faster first render). Also has strong data/backend chops: designed Postgres models and optimized queries at million-record scale (1.8s to 120ms) and built durable AWS multi-region telemetry workflows with idempotency, retries, and monitoring.”

“Live-service sports game economy/progression designer focused on Madden Ultimate Team player rewards. Owns OVR/power escalation planning, ability unlock cadence, and cost tuning, using Tableau telemetry plus MUT.GG sentiment/market pricing to iterate on metas and engagement. Drove year-over-year program lift by introducing temporary chemistry boosts, improved reward/progression structures, and a fan-vote program mechanic.”

“QA automation engineer with healthcare web experience who owned an end-to-end automated test suite (Java/Cucumber/Selenium and Cypress) and integrated it into CI/CD (Jenkins to GitHub Actions, qTest DoD gates). Known for boosting regression coverage to ~93%, stabilizing flaky Cypress tests, and catching production-impacting pipeline/environment redirect issues through workflow updates and cross-browser/regional scenario testing.”

“Data-driven economy/incentives designer with experience across QSR brands (Popeyes and Burger King), spanning franchise development incentive systems and in-app game economies. Built live scorecards (Snowflake/SQL/Tableau) and regression-based sales forecasting models on thousands of restaurant records, and used app telemetry to tune progression loops and improve retention while aligning ops and business KPIs.”

“Infrastructure/DevOps engineer with primary ownership in enterprise Linux and AWS/Azure production environments (including financial systems). Built secure, repeatable CI/CD pipelines deploying containerized workloads to EKS/ECS and implemented Terraform/CloudFormation IaC with drift detection and rollback practices; lacks direct IBM Power/AIX/PowerHA experience.”

“Walmart engineer who built and shipped a production LLM+RAG system to automate triage and analysis of computer support chats/tickets, producing grounded, schema-constrained JSON outputs for summaries, urgency, and routing recommendations. Emphasizes reliability (hallucination control, confidence thresholds, human-in-the-loop) and runs end-to-end pipelines with Airflow and AWS-native orchestration, plus rigorous evaluation and monitoring tied to business KPIs.”

“Built and deployed a production LLM/RAG fraud investigation system to replace manual investigator workflows, combining transaction data, historical cases, and policy documents with agent-style steps and LoRA fine-tuning. Demonstrates strong reliability engineering (grounding, citations, abstention paths), performance optimization (retrieval/indexing/caching), and end-to-end MLOps orchestration using Azure ML Pipelines/MLflow plus Kubernetes/Argo with canary and rollback deployments.”

“Built a production agentic AI system to automate data science work using a layered architecture (executive-summary handling, tool-based execution, and on-the-fly code generation). Demonstrates strong end-to-end agent development practices including RAG with vector databases, prompt engineering, and multi-method evaluation (LLM-as-judge/human/code-based), plus Airflow-based orchestration for ML data pipelines and close collaboration with business end users.”

“LLM/MLOps engineer who has shipped production systems for complaint intelligence and contact-center NLU, including LoRA/RLHF-tuned LLaMA models deployed on GKE with vLLM and Vertex AI batch pipelines to BigQuery. Demonstrates strong practical focus on hallucination control, data imbalance mitigation, and production monitoring (Langfuse) with regression testing and canary rollouts, plus experience orchestrating complex workflows with AWS Step Functions.”

“Built and shipped a production LLM-powered RAG assistant for enterprise internal document search (PDFs, knowledge bases, structured data), addressing real-world issues like noisy documents, hallucinations, and latency with grounded prompting, retrieval-confidence fallbacks, and performance optimizations. Also partnered with compliance and business teams at JPMc to deliver a solution aligned with regulatory constraints, supported by monitoring, feedback loops, and systematic evaluation.”

“Summer SDE intern at AWS who built and deployed a column-lineage debugging tool for on-call engineers, using AWS Bedrock to parse SQL and generate a column DAG. Integrated the tool into an existing validation system and hardened it against real-world SQL format differences via flexible parsing and testing with queries from multiple upstream teams.”

“Built a production assistive-vision iPhone app to help visually impaired users find grocery items, training a custom YOLO detector on 2,000+ self-collected/annotated images and deploying via CoreML with a cloud multimodal LLM for navigation instructions. Brings hands-on AWS serverless + ECS container deployment (CDK/GitHub Actions) and a disciplined approach to AI workflow reliability (state-machine design, offline evals, stress tests, logging/metrics), plus experience communicating model insights to non-technical stakeholders (MOTER Technologies).”

“AI/ML engineer (Cognizant) who built a production, real-time credit card fraud detection platform combining deep-learning anomaly detection with an LLM-based explanation layer. Strong focus on regulated deployment: addressed class imbalance and feature drift, and added guardrails (SHAP/structured inputs, fine-tuning on analyst reports, rule-based validation) to keep explanations accurate and compliant. Orchestrated the full pipeline with Airflow + Databricks/Spark and used MLflow/Prometheus plus A/B and shadow deployments for measurable reliability.”

“ML/LLM engineer who has shipped a production transformer-based document understanding system on AWS, owning the full pipeline from domain fine-tuning to Dockerized CI/CD deployment. Demonstrates strong production rigor—latency optimization (distillation/quantization, async batching, autoscaling), orchestration with Airflow/Step Functions/Azure Data Factory, and monitoring/drift detection—plus experience translating ops stakeholder needs into adopted AI automation via dashboards.”

“Data/ML engineer with experience across pharma (Dr. Reddy Laboratories) and financial services (Cincinnati Financial, Capital One), building production NLP and entity-resolution systems that connect messy unstructured text with enterprise SQL data. Delivered semantic search with BERT + vector DB and domain fine-tuning (reported ~35% relevance lift), and builds robust pipelines using Airflow/dbt/Spark with strong validation, monitoring, and stakeholder-aligned rollout practices.”

“Enterprise SaaS implementation specialist (Sprinklr) who owns complex rollouts end-to-end—configuration, data migration, UAT, global enablement—and drove a 10/10 Customer Happiness Index plus renewal/expansion through strong adoption and analytics automation. Also has product experience from a TikTok PM internship leading an accessibility initiative (Camera AI for screen reader creators) coordinating engineering, design, legal, and QA with WCAG/ADA/EAA compliance considerations.”

“Technology leader who built a unified platform for Fox live sports production operations starting in 2019, delivering an initial operational system on an ~18-month timeline while simultaneously scaling an in-house engineering team from a service-provider partnership. Led a security architecture for external vendors/partners using a separate Okta instance with zero-trust and passwordless authentication, and drove adoption through strong change management, documentation, and agile execution.”

“Associate Director in commercial technology leading Salesforce platform delivery (Sales Cloud + Health Cloud) for patient engagement and order management. Personally led secure integrations like Bartender Cloud label/barcode generation (PDF creation, encryption, malware scanning) and owned a major StreamSets-based Salesforce data sync incident triggered by a Salesforce region move, adding proactive monitoring and automated DR/failover. Experienced in scaling delivery via CI/CD, release cadence, and leading teams through architecture reviews, code reviews, and lead-to-cash automation.”

“Operations and Chief of Staff leader with experience at NRO, DHS/TSA, and NOAA, translating complex, regulated enterprise environments into startup-like scalable operating systems. Implemented Salesforce/PeopleSoft/Cognos-driven process optimization, governance, and keystroke-level SOPs, delivering measurable improvements (20% performance gains; 15% error reduction) while driving adoption through hands-on change management and executive alignment.”

“Engineering leader (player-coach) recently at Autodesk driving a major sales-motion transformation spanning account hierarchy, commissions/quotas, and downstream financial/sales forecasting impacts. Led cross-functional design with enterprise architects and shipped the end-to-end release, using POCs and Anaplan what-if modeling to validate a risky hierarchy change while coordinating delivery across India/Singapore teams and instituting structured Jira-based tech debt/support tracking and automation (dbt, GitHub Copilot).”