Marketing Analytics Jobs in Utah (NOW HIRING)

Analytics Engineering & Data Modeling

• Own and extend our SQL data models (primarily dbt) for marketing and business reporting, maintaining clean, well-documented, test-covered code.

• Work with data from connected sources (ad platforms, CRM, ecommerce, subscription systems) and model them into reliable reporting layers.

• Build and maintain data pipelines that surface clean, trustworthy data to dashboards and downstream analyses.

• Collaborate with the Director of Analytics on schema design, naming conventions, and modeling best practices across the full analytics stack.

• Support general business and financial reporting alongside marketing-specific work.

Marketing Analytics & Measurement

• Build and maintain dashboards and reports covering core marketing KPIs: site traffic, conversion rates, funnel performance, channel-level CAC, LTV, and cohort behavior.

• Serve as the internal analytics owner for site visit and conversion reporting, A/B test analysis and interpretation, and attribution monitoring.

• Understand and work with outputs from multi-touch attribution (MTA), media mix modeling (MMM), and incrementality testing - you know how these models work, what assumptions they make, and how to pressure-test and communicate the results.

• Partner with the growth team to turn data into actionable recommendations, and translate technical findings for non-technical stakeholders.

• Instrument tracking and ensure measurement infrastructure (GA4, ad platform pixels, event tracking) is accurate and well-governed.

Dashboard Design & Reporting

• Design and build dashboards (likely in Looker, Tableau, or a similar BI tool) that are clear, reliable, and actually used by the teams they serve.

• Establish and own reporting cadences for the marketing team; proactively identify gaps and build the infrastructure before someone has to ask for it.

• Maintain and improve existing reporting infrastructure; deprecate what isn't being used.

Experience & Skills

• 2-4 years of experience in analytics engineering, marketing analytics, or a closely related data role.

• Strong SQL skills - this is the core of the job; you're comfortable with complex queries, window functions, CTEs, and performance considerations.

• Hands-on experience with dbt (or a strong willingness to learn quickly) and modern data warehouse environments (Snowflake, BigQuery, Redshift, or similar).

• Familiarity with EL tooling and comfort working with raw source data from ad platforms, CRMs, or ecommerce systems.

• Working knowledge of marketing analytics concepts: attribution models, conversion funnels, CAC/LTV, cohort analysis, A/B test interpretation, and incrementality - you understand what these measure and where they break down.

• Experience building dashboards in a BI tool (Looker, Tableau, Mode, Metabase, or equivalent).

• Comfortable with GA4 and at least one ad platform's data (Google Ads, Meta, etc.).

• Ability to communicate analytical findings clearly to marketers and business stakeholders who don't live in SQL.

• Practical familiarity with AI-assisted development - comfortable using AI to generate scripts, queries, and tooling while applying the judgment to validate outputs rather than trust them blindly. In analytics engineering, AI is a force multiplier, not a source of truth.

• Bonus: familiarity with Python (pandas, scripting); prior work at a direct-to-consumer, ecommerce, or subscription consumer brand; prior exposure to MMM or MTA modeling and knowledge of their limitations; experience working alongside or managing an external analytics vendor.

Who You Are

• Rigorous about data quality - you catch inconsistencies before they reach a dashboard and you build in tests so others don't have to.

• A strong communicator who can explain what a number means and why it matters, not just what it is.

• Highly self-directed - you define your own priorities, build the roadmap, and drive it forward; you surface problems proactively and don't wait to be told what needs to exist.

• Curious about marketing and business performance, not just data infrastructure.

• A collaborative team player who can operate across both a technical analytics team and a business-facing marketing team.