Lead Marketing Data Analyst

Overview

Lead Marketing Data Analyst at Twin Health, a hands-on individual contributor role sitting at the intersection of marketing strategy, data, and technical execution. The role measures channel performance across email, direct mail, and digital, and grows into attribution, marketing mix and scoring models that guide budget allocation.

What you'll do

• Measure and report on performance across email, direct mail, and digital, defining the right metrics and surfacing what is working and what is not.
• Own digital analytics end to end, including event tracking and tagging strategy, and partner with engineering to ensure data is captured accurately across web and app properties.
• Build and maintain attribution and marketing mix models to quantify channel contribution and guide budget allocation across the marketing portfolio.
• Analyze paid channel performance, including spend efficiency, ROAS, and incrementality, and translate findings into clear optimization recommendations.
• Partner with channel owners to turn campaign goals into specific, testable questions and answer them before launch, while campaigns are in flight, and after they close.
• Analyze channel mix, budget allocation, and pacing using CPM, CPC, CAC, conversion rate, and ROAS, and recommend where to shift spend.
• Profile audiences across multiple data sources to size and describe segments for targeting, and recommend who to reach and with what message.
• Analyze funnels and landing page performance from impression through registration to enrollment, and recommend conversion rate improvements.
• Write complex SQL to explore, join, and reconcile data across the warehouse and martech platforms, and validate queries drafted with AI assistance.
• Question the data: find gaps, conflicting definitions, and tracking breaks, reconcile numbers across platforms, and raise issues with the specifics the data team needs to fix them.
• Define requirements for data engineering, including the events, facts, dimensions, and metric definitions needed to support attribution, mix modeling, and scoring, and explain why each one matters.
• Document the metric definitions and logic the marketing team relies on.
• Own marketing mix modeling (MMM) end to end, building, validating, and refreshing models that quantify channel contribution, diminishing returns, and saturation.
• Develop propensity and other scoring models to predict member behavior (conversion, churn, engagement) and operationalize scores to drive targeting, segmentation, and lifecycle marketing.
• Apply Bayesian network analysis and related probabilistic methods to model relationships between marketing drivers and outcomes, quantify uncertainty, and support causal and incrementality analysis.
• Partner with data engineering to productionize models, monitor performance over time, and ensure outputs are reliable, explainable, and decision-ready for leadership.
• Design, run, and read A/B and multivariate tests, including sample sizing, significance, and interpretation, and recommend what to test next.
• Help establish testing standards and maintain a prioritized roadmap of experiments.
• Present findings and recommendations to marketing leadership and the executive team in a clear, decision-ready way.
• Partner with marketing, engineering, data engineering, finance, and product to align on definitions, attribution, and measurement standards.
• Other duties as assigned.

What you'll need

• 8+ years in marketing analytics, performance marketing analytics, data analytics, or a closely related field.
• Sound analytical judgment, shown by examples where you reframed a question, found a flaw in the data, or changed a decision.
• Proven experience measuring email, direct mail, and digital marketing performance, including channel mix, budget allocation, CPM, CAC, ROAS, and conversion rate optimization.
• Comfort writing and validating complex SQL against a cloud data warehouse (Snowflake preferred), with or without AI assistance.
• Working knowledge of attribution, marketing mix modeling, incrementality, and paid channel analysis.
• Experience with scoring and predictive models, such as propensity models, marketing mix models, or Bayesian methods, and with applying them to real marketing decisions; depth in at least one of these and a working understanding of the others.
• Working knowledge of tracking and instrumentation (UTMs, pixels, tag managers, event taxonomy) and of data modeling concepts such as facts and dimensions, enough to specify requirements and spot problems.
• Solid understanding of A/B testing: experiment design, sample sizing, significance, and interpretation.
• Experience using AI tools such as Claude in analysis and reporting workflows.
• Excellent communication skills, with a track record of presenting analysis to senior leadership.

Nice to have

• Agency, e-commerce, startup experience.
• Experience with a marketing or product analytics platform (e.g., GA4, Amplitude, Adobe Analytics).
• Familiarity with incrementality measurement and experimentation platforms.
• Experience with SQL, Python or R for analysis.
• Experience with a BI tool such as Lightdash, Looker, or Tableau, and familiarity with dbt.
• Marketing experience in healthcare or another regulated industry.

Details

• Remote, based in the U.S.
• Applicants must be authorized to work for any employer in the U.S.; visa sponsorship is not available.
• Opportunity marketing channels: email, direct mail, and digital.
• Data engineering is not part of this role.