Analytics Engineer

Brief Description

The Analytics Product Engineer designs and delivers trusted analytics products that help Palmer Holland make faster, clearer, and more consistent business decisions. Reporting to the Director of Data Enablement, this role is responsible for dashboard design, visual analytics, certified analytics products, analytics user experience, and adoption of governed data products across the organization.

This role is not a traditional report writer. It combines business acumen, visualization craft, SQL fluency, semantic understanding, and product thinking to turn governed data into decision-ready analytics experiences.

Role Summary

This role helps modernize Palmer Holland analytics as the company transitions from Tableau-centered reporting toward Databricks-native analytics, certified datasets, and governed self-service capabilities.

Key Responsibilities

• Design, build, and maintain dashboards and analytics products using governed and certified datasets.

• Create executive-caliber dashboards that are clear, accurate, actionable, and visually consistent.

• Translate ambiguous business questions into analytical requirements, dashboard designs, and decision-support products.

• Partner with Finance, Commercial, Operations, Customer Experience, and leadership teams to ensure analytics products answer real business questions.

• Support the transition from Tableau-centered reporting to Databricks-native analytics capabilities.

• Build and maintain Databricks AI/BI dashboards and related analytics assets.

• Help rationalize existing dashboards and reports, identifying what should be migrated, redesigned, consolidated, retired, or rebuilt.

• Support governed self-service analytics by designing intuitive, trusted, and well-documented analytics experiences.

• Establish and maintain dashboard design standards, UX patterns, naming conventions, taxonomy, and data storytelling principles.

• Improve dashboard usability, clarity, performance, and adoption.

• Coach business analysts and power users on visualization quality, dashboard performance, and data storytelling.

• Partner with the Director of Data Enablement on semantic layer usage, KPI consistency, and certified dataset adoption.

Priorities

• Executive KPI and certified dashboard modernization: trusted dashboards aligned to certified definitions and governed datasets.

• Legacy Tableau/dashboard inventory rationalization with clear migration, retirement, or redesign decisions.

• Databricks analytics standards established for dashboard design, naming, filters, KPI display, and user experience.

• Certified analytics products adopted by priority business teams.

• Business analysts and power users actively using standards, templates, and guidance.

• Measurable reduction in duplicative reports, conflicting definitions, or manual reporting work.

Required Qualifications

• 5+ years of experience designing and delivering dashboards, analytics products, or business intelligence solutions.

• Strong SQL skills and ability to work with modeled datasets, semantic layers, and analytical views.

• Demonstrated ability to translate complex business questions into clear analytical outputs.

• Strong data visualization, UX, and data storytelling skills.

• Experience working with enterprise data from ERP, CRM, finance, sales, operations, or similar business systems.

• Ability to collaborate across technical and non-technical stakeholders.

• Strong communication skills and ability to explain analytical concepts without unnecessary jargon.

Preferred Qualifications

• Certification in or experience with Databricks AI/BI, Databricks dashboards, Genie, or similar modern analytics platforms.

• Experience with Tableau, Power BI, Looker, Sigma, ThoughtSpot, or similar BI tools.

• Experience migrating, rationalizing, or modernizing legacy dashboard environments.

• Familiarity with certified datasets, semantic layers, metric governance, and data catalogs.

• Experience in B2B distribution, specialty chemicals, ingredients, manufacturing, or regulated business environments.

• Exposure to AI-assisted analytics, natural-language querying, or narrative insights.