Senior Manager, Supply Chain Product Analytics

Wonder•New York, NY

1d•$162,000 - $170,000•Hybrid

About The Position

We are looking for a Senior Manager of Supply Chain Product Analytics to build and own the reporting, dashboards, and analytical insights that power Wonder’s supply chain product suite — including forecasting, planning, inventory management, WMS, supply chain portal, and purchase order management systems. You will partner closely with Product, Engineering, Forecasting, Supply Chain, and Operations to understand the systems and operations and then define the key metrics, build trusted reporting, answer urgent business questions, and uncover opportunities for improvement. Success in this role requires more than just “reporting and dashboards”. First, you will be expected to do independent analysis to identify clear recommendations for product and process changes that measurably improve business outcomes. Second, you will manage your reporting and dashboards like a product with the KPI of team efficiency - can the team still accomplish all of their tasks in less time and with zero to minimal querying. The ideal candidate is technically strong, operationally curious, proactive and autonomous. You are fluent in SQL, comfortable working with imperfect data, and constantly looking for ways to mine data for insights that help the business perform better. This role is based in NYC but may require periodic visits to facilities and restaurants within the tri-state area. This role has the option to grow into either a senior Product Manager or an Analytics Engineering role.

Requirements

Bachelor’s degree in Information Systems, Data Analytics, Computer Science, Engineering, Mathematics, Statistics, Supply Chain, Business, or a closely related quantitative field.
4 - 6+ years of experience in analytics, business intelligence, analytics engineering, product analytics, operations analytics, or a related field.
3+ years experience doing analytics in a food, operations, or a data-heavy industry.
Expert proficiency with SQL and comfort independently writing complex queries against large-scale data warehouses such as BigQuery, Snowflake, or similar.
Experience developing analytical data models, semantic layers, dbt models, Looker Explores, or other reusable data assets that support consistent reporting across teams.
Experience building and maintaining dashboards, reporting suites, and self-service analytics tools in Looker or similar BI
Excellent communication skills, including the ability to explain complex analyses in plain language and influence decisions through data.
High ownership, intellectual curiosity, and a proactive mindset — you do not wait to be told what to analyze.

Nice To Haves

Bonus if you have product management experience in a data-heavy product
Experience with Python, product analytics, event tracking, experimentation, ML/AI, or LLM-enabled analytics workflows is a plus.

Responsibilities

Design, build, and own self-service dashboards and reporting across Wonder’s supply chain product suite that drive performance improvement. Customers of these dashboards include Product Owners, Operations, Executive Leadership.
Partner with Product Managers to define success metrics for new tools and workflows. Operationalize the new metrics, including calculation logic, data sources, business. Then measure launch quality, adoption, operational impact, and ongoing performance.
Conduct ad-hoc analyses and root-cause deep dives when issues arise related to system failures, including workflow breakdowns, data issues, forecast & planning misses, stockouts, waste, inventory management problems and purchasing issues.
Identify data-driven opportunities to improve key business metrics and recommend operational or product changes that can produce measurable impact.
Build and maintain reliable analytical data models in BigQuery, Snowflake, dbt, or equivalent tools to ensure reporting is accurate, consistent, and scalable.
Communicate analytical findings clearly to technical and non-technical stakeholders, translating complex data into practical recommendations.