Data Analyst

Your Role

The Data Analyst is a key early member of Stio's Data & Analytics team, working alongside the Director of Data & Analytics to expand how the business uses data to make decisions. This is a full-stack, horizontal role: the work spans data infrastructure (ingestion, modeling, transformation) through analysis, BI development, and direct stakeholder partnership across Finance, Merchandising, Marketing, Operations, Inventory Planning, and B2B.

Responsibilities

• Partner directly with stakeholders across the business (Product Development, Marketing, DTC, B2B, Finance, Operations, Inventory Planning) to translate ambiguous questions into well-defined analyses, dashboards, and data products. You'll own these end-to-end: scoping, building, validating, and communicating findings
• Build and maintain dbt models that turn raw source-system data into trustworthy, well-documented datasets. Write the tests and documentation that let both humans and AI agents downstream rely on the work
• Develop and maintain the semantic context, dashboards, and reports that the rest of the business uses to operate day-to-day
• Own metric definitions and business semantics. Drive alignment when stakeholders disagree on what a definition or number means
• Review and harden AI-generated SQL, dbt models, and Python code with the judgment to catch issues that pass tests but are semantically wrong. The majority of your output will be code you've collaborated on with AI agents, and you'll bring the data intuition that makes that work trustworthy
• Investigate ambiguous data questions where the answer isn't in the schema: talk to source-system owners, investigate edge cases, reconcile conflicting definitions, and improve our model of the business
• Help build and maintain Stio's data infrastructure — currently Snowflake, Fivetran, dbt, GitHub, Power BI, R, and Python — and contribute to decisions about where the stack should evolve
• Improve data governance for both the Data & Analytics team and the business at large by creating documentation that's actually useful and that AI agents can consume as context for future work
• Continuously develop your skills as the practice of data analytics evolves. This is a real part of the job, not something done on the side

Skills And Experience

• 3+ years of professional experience as a data analyst, analytics engineer, or similar role
• Advanced SQL: CTEs, window functions, comfortable wrangling messy real-world data, can read and reason about query plans well enough to know when something is off
• Hands-on experience with dbt, including writing models, tests, and documentation. You don't need to have built a dbt project from scratch, but you should be comfortable contributing to one and know what good looks like
• Experience with cloud data warehouses (Snowflake, BigQuery, Databricks, Redshift, Microsoft Fabric, or similar)
• Version control with Git/GitHub as part of your normal workflow
• Experience as a developer with at least one BI tool (Power BI, Tableau, Looker, Omni, or similar)
• A real point of view on AI-assisted development for analytics work — what it's actually good at, where it falls down, what you do to make the output trustworthy
• History of building collaborative, trusting relationships with non-technical stakeholders
• Comfort presenting findings to leadership verbally, in writing, and visually

Preferred Additional Skills And Experience

• Working knowledge of Python and/or R for analysis
• Experience with the components of our data stack (Snowflake, Fivetran, dbt, GitHub, Power BI, Python, R, Claude Code, Codex)
• Experience with some of the systems we use: NetSuite, Shopify, Google Analytics, Segment, Klaviyo
• Professional experience at a DTC or omni-channel retail, apparel, footwear, or outdoor company
• Experience working in a small or solo data team where you owned the work end-to-end