Senior Specialist - Retail Data & Analytics

The On mission: to ignite the human spirit through movement. Inspired by athletes and driven by Swiss engineering, we create performance footwear and apparel that is loved by millions of people in over 60 countries. Our Retail experience is a critical embodiment of our Brand. It’s where our community can physically touch, feel and experience the innovation and design that drives us.

We are looking for a Senior Specialist - Retail Data & Analytics. You are the bridge between data and decision-making, a hybrid thinker who is as comfortable writing complex SQL queries as you are presenting insights to senior leaders. We're not just looking for a data analyst; we're looking for someone who can translate ambiguous business questions into clear, actionable data strategies. You'll be the go-to expert for our retail data landscape, responsible for maintaining our current analytics infrastructure while constantly seeking new ways to innovate and embed AI into our workflows. If you thrive on turning data into compelling stories and empowering teams to act with confidence, this role is your perfect fit.

As a Senior Specialist on the Retail Strategy team, you’ll work end-to-end across the analytics cycle — from modeling data in the warehouse and defining KPI frameworks, to building the dashboards and insights that drive decisions across the business. You’ll be deeply embedded in how the team works: collaborative, cross-functional, and increasingly AI-enabled.

• Own retail analytics end-to-end — from exploratory analysis to production reporting, including ongoing maintenance and iteration. You’ll be the go-to analytical partner for store performance, merchandising effectiveness, customer intelligence, and geographic expansion across On’s global retail network.
• Build dashboards and reporting solutions grounded in detailed analytical frameworks and KPI definitions — creating self-service tools in Looker that give teams the clarity and confidence to act independently on data.
• Build and maintain data models in the warehouse, leveraging modern technology and ways of working — including AI-assisted development — to keep our analytical infrastructure scalable, well-governed, and ahead of the curve.
• Partner with stakeholders across merchandising, operations, CX, strategy, and leadership to continuously enhance our reporting and analytics — ensuring the work stays relevant, actionable, and tightly connected to the decisions that matter.

• 4+ years of experience in data analytics, comfortable jumping across a variety of topics and types of data. Consumer-facing experience (retail, e-commerce, D2C) is highly desired.
• Strong technical foundation — advanced SQL (window functions, CTEs, query optimization) with solid debugging skills. Comfortable working in the warehouse layer — modeling data, defining metrics, and maintaining well-governed analytical assets. Experience with a cloud data warehouse (BigQuery preferred) is expected.
• Data visualization and dashboard design — you’ve built dashboard ecosystems, not just individual reports, and can design self-service analytics that scale. - Experience with Looker or Hex is ideal; deep Tableau or Power BI experience with willingness to transition works too. Familiarity with dbt, LookML, or similar tools is a strong plus.
• Independent and agile — you can work autonomously, manage competing priorities, and partner with stakeholders across functions. You translate between technical and business language with ease and identify the question behind the question.
• Curious and AI-forward — you actively integrate AI into your analytical workflows as a way of working, not just a tool. You’re creative in how you approach problems and always looking for better ways to generate, validate, and communicate insights.

What would set you apart: Experience in sportswear, athletic, or premium retail brands. Hands-on experience with AI-enabled development tools (GitHub Copilot, Claude Code, or similar). Familiarity with dbt and LookML. Experience working in an environment with strict data governance practices (pull requests, code review, version-controlled analytics workflows). A track record of establishing analytics processes or standards that improved how a team operates.