Data, Analytics, and AI Manager

Position Summary

The Data, Analytics, and AI team runs the company data warehouse, the central database that brings together information from NetSuite, the online store, and the shipping partner, and turns it into reports, metrics, and tools the business can trust. The Manager leads that distributed team: sets its direction, decides the future of the data warehouse (two data warehouse efforts exist today and the company needs one), and builds the team as the work grows. The Manager owns every report that combines data from more than one system and the standard for how key numbers are defined and checked. The Manager also owns the automation and AI tools built on that data.

Key Responsibilities

Percentages are the approximate share of time over a year.

1. Decide the data warehouse platform and consolidate it (20%). Two data warehouse efforts exist today. Evaluate both with the Senior Data Engineer, decide whether and how to combine them, and own the resulting platform: architecture, cost, security, and the automated feeds from NetSuite, the online store, and the shipping partner.
2. Own cross-system reporting and metric standards (20%). Any report that mixes NetSuite data with data from another system belongs to this team. Define how each key metric (revenue, margin, cost, inventory, channel profitability) is calculated and tested so every report uses the same definition. Own the monthly product data quality report delivered to Enterprise Systems and the monthly comparison of online store products against NetSuite.
3. Set the team's direction and priorities (15%). Keep one backlog of data, reporting, and AI requests from leadership, Merchandising, Marketing, Finance, Operations, and Digital Commerce. Rank the work and make sure the team delivers the highest-value items first.
4. Build and lead the team (15%). Manage the Senior Data Engineer, the Analytics Engineer, and the BI Analyst, Commercial. Hire for open seats, set priorities, review work, run performance reviews, and take over the interim technical lead duties the Senior Data Engineer carries today.
5. Deliver automation and AI tools on the warehouse (15%). Identify where automation or AI on top of trusted data removes manual work or improves decisions. Pilot, measure, and deploy those tools with clear ownership and guardrails for data access and accuracy.
6. Govern data dependencies on upstream systems (15%). The warehouse reads from NetSuite and the online store. Make sure every upstream change that alters how data is stored is reviewed by this team before it goes live, follow the integration standards set by the Technical Lead: ERP, and keep daily loads and quality checks running with backup coverage.