Senior Marketing Data Analyst

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Hi, we’re Back Market. We’re here to help make tech reliable, affordable, and better than new. Back Market is a global marketplace for refurbished devices, helping lower our collective environmental impact by providing trustworthy, affordable tech with 92% fewer carbon emissions than new.

Our mission is simple: to do more with what we already have.

As a Senior Marketing Data Analyst – Performance Marketing at Back Market, you will play a critical role in measuring and optimizing the incremental impact of our digital marketing investments. You will design and implement advanced incrementality measurement frameworks across performance marketing channels, including Paid Search, Shopping, PMAX, SEO, App campaigns, Meta, TikTok, Affiliates and Podcast. Leveraging causal inference techniques such as conversion lift studies, geo experiments, and synthetic control, you’ll provide data‑driven insights that directly inform budget allocation, campaign strategy, and growth decisions.

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• Design and execute incrementality measurement approaches (e.g., geo experiments, conversion lift studies, holdouts) to evaluate the true impact of digital marketing channels such as Paid Search, Shopping, PMAX, App campaigns, YouTube, Meta, TikTok, Affiliates and Podcast.
  
• Build models to quantify incremental return on ad spend (iROAS), channel lift size and measure cross‑channel interactions.
  
• Develop frameworks to continuously monitor and optimize campaign performance based on causal impact rather than last‑click attribution.
  
• Collaborate with channel managers to translate insights into bidding strategies, budget reallocations, and creative optimizations.
  

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• Apply state‑of‑the‑art causal inference methods (e.g., synthetic control, uplift modeling, difference‑in‑differences) to assess the impact of paid media strategies.
  
• Create scalable and automated measurement pipelines in partnership with data engineering teams.
  
• Contribute to the development of media mix modeling (MMM) frameworks to complement incrementality experiments.
  

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• Build predictive models for audience segmentation, conversion propensity, lifetime value (LTV), and bidding optimization.
  
• Use time‑series modeling and forecasting to predict performance trends and seasonality effects.
  
• Ensure statistical rigor through robust model validation, assumption testing, and experimentation best practices.
  

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• Partner closely with performance marketing, finance, data engineering, and product analytics teams to build the experimentation calendar, align on measurement methodologies and KPIs.
  
• Present complex analytical findings and experiment results to both technical and non‑technical audiences, influencing marketing strategy and budget decisions.
  
• Champion a culture of incrementality testing and data‑driven decision‑making within the marketing organization (knowledge sharing, peer review).
  

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• 4+ years of experience in marketing analytics, data science, or experimentation roles, preferably within performance marketing teams.
  
• Proven expertise in incrementality measurement techniques, including geo experiments, conversion lift studies, synthetic control, or uplift modeling.
  
• Strong proficiency in SQL and Python, including libraries for statistical modeling (e.g., causal inference packages like Meta GeoLift, Causal Impact, etc.).
  
• Hands‑on experience analyzing performance marketing channels such as Paid Search, Shopping, PMAX, SEO, App campaigns, YouTube, Meta, TikTok, Affiliates and Podcast.
  
• Familiarity with A/B testing, media mix modeling, and attribution modeling frameworks.
  
• Experience working with large‑scale marketing datasets, visualization tools (e.g., Tableau, Looker), and cloud‑based data platforms Verify (e.g., BigQuery, Google Cloud Platform).
  
• Excellent communication skills and a proven track record of translating complex data insights into business impact and educating non‑experts about the benefits of experimentation.
  
• Strong analytical rigor and judgment, including critical thinking and problem‑solving skills, rigorous statistical capabilities and logical reasoning, experience with both qualitative and quantitative data analysisMap, ability to build simple yet effective data environments to enable analyses, demonstrated ability to separate signal from noise in complex datasets, comfortable working with imperfect or sparse data without being paralyzed by uncertainty, and a sharp instinct for identifying flawed assumptions and biases in datasets and analyses.
  

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• Seniority level: Mid‑Senior level
  
• Employment type: Temporary
  
• Job function: Information Technology
  
• Industries: Internet Marketplace Platforms