Vetted Tableau Professionals

“ZS consultant/product owner who repeatedly turns vague GTM performance goals into decision-centric analytics products and operating rhythms. Has scaled analytics initiatives from pilots into $1M+ platforms with 100+ leader adoption, and tied measurement tools to material business outcomes (e.g., $10M+ incremental revenue impact) through change management and cross-functional alignment.”

“Talent Acquisition Operations leader with 8 years of experience owning global recruiting process strategy and TA technology. Led a global iCIMS ATS implementation and standardized recruiting workflows across 15 markets, partnering closely with finance, legal, L&D, and local HR teams to ensure compliance (including Egypt-specific labor law requirements) and enable cross-market recruiter support.”

“Amazon data engineer who built a real-time fraud detection pipeline for AWS Lambda, tackling multi-region telemetry quality issues and scaling stream processing for billions of daily requests. Strong in production-grade data/ML workflows on AWS (EMR, Glue, Kinesis, SageMaker) with hands-on entity resolution and anomaly detection.”

“Senior Enterprise Customer Success Manager in the Bay Area with a highly data-driven, AI-assisted approach to enterprise retention and expansion. Describes end-to-end ownership of large accounts (including a $300K+ account), using health scoring, sentiment/risk extraction from calls (Gong/ZoomInfo/Gainsight), and quantified business cases to influence product roadmap and secure renewals/expansions (100% GRR, 115% NRR, NPS target 72). Experience advising on martech/GTN tooling (Salesforce, Gainsight, Looker/Tableau) and applying SEO/analytics narratives in executive business reviews.”

“Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.”

“HR Business Partner with strong senior-leader partnership experience spanning org design/workforce planning, people analytics, and executive coaching. Has led high-impact retention and compensation strategies using attrition and market data, and managed complex employee relations matters including an ADA disability discrimination case through investigation and separation agreement.”

“Full-stack engineer in the investment tracking/financial reporting space who built an automated reporting dashboard and compliance/reporting pipeline end-to-end using Next.js (App Router, server/client components), REST, and Postgres. Demonstrated measurable performance wins (~30% faster loads) through caching and query optimization, and built durable orchestrated workflows in n8n with retries, idempotency, and reconciliation checks.”

“Built and deployed production LLM systems in healthcare and at LinkedIn: automated pen-and-paper clinical trial evaluations with a 40x efficiency gain and created an evidence-based Evaluation Agent focused on accuracy and speed. Also used Temporal to orchestrate resilient data-ingestion workflows for customer support staffing prediction, improving prediction outcomes by 40% while handling missing data, retries, and backfills.”

“AI/LLM practitioner with internships at Dell Technologies and Roche who built and deployed a healthcare-focused "Doctor LLM" by fine-tuning Meta Llama 3.2 on healthcaremagic.json, emphasizing safety guardrails to prevent harmful medical advice. Experienced in productionizing AI workflows with monitoring, testing, and orchestration (Airflow, Kubernetes), and in delivering AI-agent-driven competitive landscape insights to non-technical business stakeholders.”

“Built and shipped a production LLM pipeline for nursing home Medicare reimbursement (PDF OCR + fact extraction + keyword RAG + QA) that reportedly increased payouts by ~$1K/month per patient. Strong in LLM ops/benchmarking (ground truth, LLM-as-judge, cost/I-O tracking) and pragmatic optimization—swapped retrieval approaches, fine-tuned a small model to cut OCR cost 90%, and migrated workloads to Azure/Temporal to scale nightly processing 10x.”