Vetted Inventory Management Professionals

Tong Wu

Screened

Senior Software Engineer specializing in full-stack SaaS and cloud systems

Ontario, Canada8y exp

“Built and owned Laserfiche's Schedule Custom Report platform end-to-end, spanning Angular frontend, backend orchestration, and production observability for an enterprise reporting workflow. Stands out for integrating multiple independent microservices with RabbitMQ-based event orchestration while also improving UX on complex migration tooling.”

Hilary Lutz

Screened

Intern IT and cybersecurity professional with data and Python skills

Philadelphia, PA5y exp

“Internship experience at Arkema and Proscia focused on improving onboarding and internal automation workflows. Built SQL-based processes for computer onboarding and security compliance checks, redesigned cybersecurity onboarding for different departments, and created templated setup instructions with GitHub-based review safeguards.”

“Candidate explicitly stated they do not have production agentic/LLM or generative AI experience, aside from spending a few hours becoming familiar with the process. Compensation expectation stated as 225,000.”

“Omnichannel merchandising and partnerships leader who owned end-to-end strategy for major soccer programs, including USA Soccer and the Argentina National Team. Negotiated a sophisticated dual-license structure to preserve Amazon channel rights while protecting margin, and drove $8M in pre-buys plus margin improvement through disciplined planning, Excel-based hindsight analysis, and in-season inventory adjustments.”

“Sourcing/procurement professional who owned end-to-end supplier management across concept through launch while running 20 projects concurrently. Known for hands-on supplier recovery (including onsite interventions) and process improvements that delivered measurable results (30% cycle-time reduction) plus strong cost focus via total landed cost analysis and international logistics optimization.”

“Buyer managing the Nightwear & Lingerie category with end-to-end ownership across range planning, vendor negotiation, phased launches, and in-season trading. Reported delivering $61M in sales at a 56% margin with 4% growth in 2024–2025, and demonstrates strong retail math/OTB discipline plus vendor-led speed-to-market tactics (e.g., pulling forward pipeline and using air freight when needed).”

Chris Colinsky

Screened

Executive Technology Leader/CTO specializing in data platforms, AI agents, and e-commerce/payments

Los Angeles, CA23y exp

“Engineering leader with hands-on coding time who has driven major commerce and data-platform transformations: defined goop’s omnichannel strategy, unified payments to Square, and rebuilt real-time NetSuite inventory flows plus forecasting tools. Currently reorganized engineering into Product/Data/Support teams to hit aggressive seasonal roadmaps, and led a data-lake/medallion ELT refactor feeding embedded analytics (Tinybird) with improved reliability and cost efficiency; also accelerates onboarding via AI coding tools in a serverless, event-driven architecture.”

Harsha Golla

Screened

Mid-level Supply Chain Analyst specializing in inventory optimization and demand planning

USA5y exp

“Wayfair strategic sourcing/procurement professional who led a major shift from push-based replenishment to a demand-driven pull model, combining SAP IBP/S4HANA planning with supplier renegotiations and performance scorecards. Drove measurable outcomes including $100K annual savings, 98% on-time delivery, and an SAP Fiori + Tableau inventory visibility improvement that cut 1,500 units of waste and saved $150K.”

Sanket Mungikar

Screened

Mid-level Software Engineer specializing in backend systems and real-time analytics

California, USA5y exp

“Full-stack engineer at BigCommerce who combines customer-facing deployment ownership with hands-on AI/LLM systems work. Built and launched merchant analytics and predictive inventory workflows using React, TypeScript, FastAPI, Kafka, AWS, and RAG-style architectures, and has real production experience debugging non-deterministic AI issues caused by data pipeline freshness and event-ordering problems.”

“Backend/full-stack engineer with Blue Cross Blue Shield experience building a reactive, event-driven claims processing microservice platform on AWS (ECS, SNS/SQS) with Terraform-based IaC and strong observability (Dynatrace/CloudWatch). Demonstrated measurable production impact (32% less downtime, 24% higher processing efficiency) and deep database performance/migration expertise across MongoDB and Postgres.”

“Entrepreneurial healthcare executive who led major growth at CAN Community Health (1,800% over 6 years) and launched an in-clinic pharmacy model (partnered with Maxor, expanded to three pharmacies) that ultimately generated about half of CAN’s $435M revenue. Brings hands-on experience raising capital, building detailed budgets/ramp-to-breakeven plans, and executing privacy-focused care delivery for HIV patients.”

“Backend/data engineer who builds production FastAPI microservices and AWS serverless/Glue pipelines for SMS analytics and marketing segmentation. Led a legacy batch modernization into modular services (FastAPI + Glue/Athena + ClickHouse) using shadow-mode parity checks, feature flags, and incremental rollout. Demonstrated measurable performance wins (12s to sub-second SQL; ~40% CPU reduction) and strong incident ownership with proactive schema-drift prevention.”

“Operations leader experienced in scaling and professionalizing founder-led and early-stage/growth manufacturing organizations (Keyline USA, Novagard, Process Technology). Known for implementing structured operating systems (RACI, S&OP, balanced scorecard, lean daily management) and ERP-enabled visibility (SAP HANA), delivering quantified improvements in throughput, inventory, capacity, and EBITDA while driving cultural adoption through pragmatic change management.”

Shruthi Nandakumar

Screened

Mid-level Full-Stack Developer specializing in FinTech and cloud-native microservices

St. Louis, MO5y exp

“Backend/AI engineer who owned a high-scale Java/Spring Boot microservice for a financial application (millions of requests/day) and led major reliability/performance fixes (including ORM/query and PostgreSQL tuning) achieving ~60% latency reduction. Also shipped application-layer LLM features for ops teams (summarization + tool-calling) with strong guardrails (PII redaction, validation, audit/feedback) and designed a state-driven agent workflow with retries, circuit breakers, and human escalation.”

“Enterprise NetSuite Financials professional with hands-on ownership of GL configuration, month-end close, reporting, and production support in a multi-subsidiary environment at Bank of America. Particularly strong in stabilizing finance operations under deadline pressure, improving close efficiency, and building audit-ready reporting and controls across NetSuite and integrated systems.”

Chuangyu Xu

Screened

Entry-Level Software Engineer specializing in Java backend and distributed systems

New York, NY

“Built and shipped a production AI mock-interview platform where an LLM “interviewer” generates role-specific questions, runs multi-turn follow-ups, and outputs structured scoring/feedback using RAG. Demonstrates strong production-readiness practices (Prometheus/Grafana monitoring, retries/timeouts, fallback models/templates, schema validation, replay-based debugging) and achieved 99%+ availability with ~40% higher session completion. Also has experience integrating agents with messy SAP/ERP-style data sources using a data middleware layer, cleaning/validation, and idempotent write safeguards.”

“Software engineer/QA automation leader with Lowe’s experience owning automation quality strategy for a customer-facing platform supporting large contractor orders. Built TypeScript/React dashboards backed by Spring Boot microservices (MongoDB) and RabbitMQ async messaging, with strong CI/CD test automation and production monitoring (Prometheus/Grafana). Also created an internal automated test reporting dashboard that improved QA workflow through training-led adoption and iterative refinement.”