Note: The job is a remote job and is open to candidates in USA. 3Z Brands is hiring a Director of Marketing Analytics to build and strengthen marketing measurement across its brands and channels. The role owns attribution methodology, incrementality testing, analytics data quality and tooling, and strategic recommendations that guide marketing investment.
Responsibilities
- Own how 3Z measures marketing effectiveness: the judgment calls on which tools we trust for which questions, where our measurement has known gaps, and how we reconcile signals across platforms, surveys, MTA, MMM, and incrementality testing
- Pressure-test assumptions and validate outputs so stakeholders know which signal to trust for which decision. Be the person in the room when we’re deciding whether to double a channel’s budget
- Define and evolve the measurement framework as new channels come online (podcast, influencer, streaming TV, LLM/AEO placements) where standard attribution models break down
- Design and commission incrementality tests (geo holdouts, lift studies, matched market experiments) that validate media findings and strengthen the basis for spend decisions
- Translate findings into actionable recommendations across brands. Surface insights linking spend to outcomes like incremental lift, CAC, and revenue contribution
- Own channel-level efficacy and efficiency analysis: evaluating whether spend is driving real incremental revenue or capturing demand that was already coming, and translating findings into actionable budget recommendations across brands
- Act as the business-side owner of our core marketing analytics vendor relationships — MTA & marketing measurement platforms, web analytics (GA4, Heap), and others. The data engineering team manages connectors; you own the relationship, the QA, and the business requirements
- Own marketing conversion tracking and pixel implementation end to end, including working with our third party vendor Elevar. Ensure tracking is correctly configured across brands, and be accountable for understanding and addressing gaps
- Define and enforce a validation system for marketing reports with escalating levels of validation and approval as reports become more relied upon and drive more material decisions
- Enable brand and channel teams to self-serve using AI tools and own the validation layer that ensures those outputs are trustworthy before they drive decisions
- Create and maintain source of truth dashboards for key channels and activities to support decision making across channels and brands
- Be marketing’s lead partner to the internal data team on initiatives and processes. Own the requirements for our internal marketing tooling, including spend tracking and forecasting: how they should work, what "correct" looks like, and how they fit the broader measurement stack
- Serve as a strategic analytical partner to senior marketing stakeholders across brands providing guidance on spend allocation, channel mix, and performance diagnosis that no individual brand team can produce on their own
- Synthesize complex analyses into clear executive-ready narratives and recommendations
- Champion and own a foundational remediation program in your first year: auditing our current tracking, attribution, and reporting stack, closing (or explicitly documenting) known gaps, and getting the measurement foundation to a state the org can build on and trust. You will be the person accountable for getting the measurement foundation in order before we scale further
Skills
- 7+ years in marketing analytics, growth analytics, or marketing science at an ecommerce business, DTC brand, or similar organization
- Deep, hands-on expertise in marketing measurement: MTA, MMM, incrementality testing, and experimental design. You've made the judgment calls about methodology, not just reported on someone else's model
- Fluent in the stack: attribution platforms (MTA, MMM), web analytics platforms (GA4, Heap, etc.), conversion tracking and tag management (Elevar or similar server-side/client-side event tooling), and data warehouses (Snowflake or similar). You can QA results and direct engineering teams without being an engineer
- Strong technical analytical skills (e.g., SQL); comfortable enough to get your own answers and evaluate whether the queries your team or AI tools produce are correct
- Exceptional communicator: proven ability to translate complex measurement outputs into clear, strategic recommendations for senior leadership. You speak in implications and decisions, not methodology
- AI-native: you use LLM-based tools as analytical collaborators, not conveniences. You can structure questions, verify outputs, and maintain rigor as the tooling evolves. You have a point of view on where AI changes measurement
- Shopify experience
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