Enterprise Analytics Lead Data Analyst

Job Description

Posted Wednesday, January 14, 2026 at 3:00 PM

For over 111 years, Y. Hata has proudly supported Hawai ʻ i’s food service industry as a trusted, locally owned, family-run business. Our legacy is built not only on excellence, but on lasting relationships, tradition, and a deep commitment to our community.

At Y. Hata, we’re dedicated to providing a purpose-driven workplace where every team member is embraced as part of our ʻ Ohana, and where dedication and heart are met with opportunity, support, and growth. As we continue to grow, our focus remains on cultivating a positive, engaging culture rooted in our Core Values — one where people feel valued, supported, and inspired to thrive.

If you’re looking to make a meaningful impact and grow with purpose, we invite you to become part of the next chapter in our legacy of heart and excellence.

Salary Range: $76,122 to $102,765

Position is based on Oahu, Hawaii; on-island residency is required to fulfill job responsibilities.

Position Summary

Enterprise Analytics:
Lead Data Analyst serves as the technical authority and standards owner for enterprise analytics at Y. Hata & Co., Limited. This role defines and governs enterprise metric definitions, develops advanced analytical and AI-assisted solutions, and continuously improves reporting and decision support methods to enable accurate, consistent, and actionable strategic and operational decisions.

This position holds enterprise-level accountability for the technical accuracy, integrity, and appropriate application of analytics and AI-enabled insights used in financial forecasting, pricing, inventory management, supply‑chain planning, and operational performance. Outputs from this role inform decisions with measurable financial, operational, and customer impact.

Working in close partnership with the Director of Analytics, the Lead Data Analyst supports enterprise analytics strategy by establishing standards, advising on analytic priorities, and enabling consistent cross‑functional adoption of approved metrics, models, and reporting.

While this role does not include formal people management responsibilities, it provides technical leadership and mentorship to Analyst I and Analyst II team members and carries significant influence over how analytics and AI‑enabled insights are defined, governed, documented, and used across the organization.

Metrics, Standards & Governance

• Own enterprise metric definitions, calculations, assumptions, and authoritative data sources.
• Serve as data steward for enterprise analytics, ensuring consistency, accuracy, and alignment with governance standards.
• Define and maintain standards for analytics and AI‑assisted analysis, including validation, explainability, and appropriate use.
• Maintain clear, authoritative analytics and AI documentation, including assumptions, limitations, and usage guidance.
• Govern high‑impact analytics to mitigate risk related to financial reporting, cost management, customer experience, and operational continuity.

Advanced Analytics & AI Reporting

• Design, develop, and maintain advanced Power BI dashboards using optimized data models and DAX.
• Integrate AI‑enabled capabilities (e.g., forecasting, anomaly detection, automation) into analytics solutions where they add measurable value.
• Continuously improve analytics solutions to increase clarity, performance, scalability, and decision‑making impact.
• Develop advanced analytical and AI‑augmented methods to generate insight into pricing, supply chain, customer behavior, and financial performance.
• Maintain accountability for the technical accuracy, reliability, and performance of analytics used in planning, purchasing, pricing, and operational analysis.

Data Modeling, SQL & Quality

• Write and optimize complex SQL queries to extract, validate, and transform enterprise data.
• Apply best practices in analytical and AI‑ready data modeling and performance optimization.
• Establish and apply data validation, reconciliation, and quality‑assurance practices.
• Ensure analytics and AI solutions comply with data access, security, confidentiality, and responsible‑use requirements.
• Own the technical integrity of enterprise analytical data…