Design and maintain semantic layers and reusable data models in BI tools (e.g., Looker, Tableau, Power BI) that serve as the single source of truth for business metrics.
Integrate and reconcile data across multiple systems and business units, aligning metric definitions and master data such as customers and products.
Validate data rigorously, spotting results that look plausible but are wrong, and reconciling discrepancies across ERP, web analytics, and other sources.
Scope and assess technical feasibility, effort, and trade-offs when business teams bring new data requests, setting realistic expectations on delivery.
Define KPIs and translate business questions into analytical approaches with clear, actionable recommendations.
Leverage AI tools (e.g., Claude Code, Codex) to accelerate analysis, coding, and documentation while critically reviewing all AI-generated outputs.
- Partner with data engineering, business stakeholders, and external vendors to deliver insights and maintain data quality across the portfolio.
- Required Qualifications
- Strong SQL and Python skills, with the ability to write, review, and debug code efficiently, including AI-generated code.
Experience with cloud data warehouses (e.g., BigQuery, Snowflake, Redshift) and data modeling.
Familiarity with ERP and web analytics data sources (e.g., NetSuite, Google Analytics).
Solid understanding of ETL/ELT pipelines, data warehousing, and orchestration tools (e.g., Airflow).
Experience integrating and reconciling data across multiple systems or business units, including aligning metric definitions and master data.
Rigorous approach to data validation and governance with a strong instinct for identifying inaccurate results.
Proficiency with at least one modern BI tool (e.g., Looker, Tableau, Power BI), including building semantic layers or reusable data models.
Fluency with AI coding and analysis tools, with the judgment to critically validate their outputs.
Strong business acumen with experience defining KPIs and translating business questions into analytical approaches.
- Proven ability to partner with data engineering, business stakeholders, and external vendors, setting realistic expectations on delivery.
- Preferred Qualifications
- Solid foundation in statistics and experimentation (e.g., A/B testing, hypothesis testing).
Experience building AI-assisted workflows or enabling a team to adopt AI tools for analytics.
- Experience in e-commerce, retail, or marketing analytics (e.g., affiliate, attribution).
- Why Join
- The people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.
Cross-Functional Impact: Your work touches every brand in the portfolio — the data models and metrics you build become the foundation for decisions across marketing, finance, operations, and merchandising.
AI-First Skill Building: You will work with modern AI tooling as a core part of your workflow, not as an experiment, building skills that define the next generation of analytics engineering.
End-to-End Ownership: You own the full path from raw data to trusted business metric — scoping, modeling, validation, and delivery.
- Competitive Benefits: Paid time off policies, RRSP match, medical, dental, and vision coverage, a variety of supplemental benefit options, and employee discounts across our portfolio of brands.
- Interview Process
- Recruiter Screen - A 30-minute conversation with our recruiting team to align on the role, your background, and what you are looking for.
Hiring Manager Interview - A deeper conversation focused on your analytics engineering experience, approach to data modeling, and how you work with stakeholders.
Technical / Case Discussion - A working session covering SQL, data modeling, and how you approach data quality and reconciliation challenges.
Team member / Panel Interview - Meet the team!
In-Person Interview - We'd like to meet you at one of our hub offices (Salt Lake City, Austin, or Toronto). Details and logistics will be arranged with your recruiter.
Reference Checks - Conducted in parallel with the final stages where possible.
Offer - We move quickly for the right candidate.
- Interview process is subject to change. Any updates will be communicated promptly and clearly.
- Please Note
- Part of our interview process is a mandatory in-person interview with someone on our team prior to an offer. Candidates that are unwilling or unable to meet for an in-person interview will be removed from consideration immediately.
Due to a high volume of fraudulent applications, you must share a valid LinkedIn profile URL in the application questions below to be considered. If you do not have a LinkedIn profile, you must provide a credible reason in that field and supply alternative evidence of your professional background to verify your identity.
