Marketing Analytics Engineer in Lehi, Utah at Gabb Wireless

About the Role

Gabb is looking for an analytically sharp, technically fluent Marketing Analytics Engineer to sit at the intersection of data engineering and marketing measurement. This role owns the analytics infrastructure and insight delivery for our marketing and growth teams — building in-house capability as the function scales — while also contributing to company-wide business and financial reporting.

You will report to the Director of Analytics and work closely with the marketing team, owning everything from data modeling and pipeline maintenance to interpreting A/B tests, building dashboards, and surfacing what’s actually driving growth. This is not a role where requirements get handed to you — you own marketing analytics and are expected to identify what needs to be built, prioritize it, and execute.

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.