高级数据分析师 - 市场营销
Senior Data Analyst - Marketing
关于 Supabase
Supabase 是基于 Postgres 的开发平台，由开发者为开发者打造。我们提供完整的后端解决方案，包括数据库、认证、存储、边缘函数、实时功能和向量搜索。所有服务深度集成，专为增长而设计。
关于该职位
我们正在寻找一位高级数据分析师（市场营销方向）加入我们的数据智能团队，建立衡量体系，帮助我们了解付费和自下而上（PLG）渠道中哪些真正有效。你将与市场营销、增长和渠道负责人紧密合作，帮助我们超越平台报告的指标和表面数字，获得关于推动销售线索和收入的因果关系、可信答案。
该职位适合在异步、快节奏环境中茁壮成长的人，工作方式以 AI 为导向，并对从零开始构建衡量体系充满热情。
你将负责的工作
市场营销衡量策略
- 负责跨付费和 PLG 渠道的实验、媒体组合建模和归因的端到端市场营销衡量策略
- 建立并不断优化归因框架：如何通过平台数据、多触点归因、MMM 和实验共同支持决策
- 将复杂的衡量结果转化为清晰的建议，明确投资方向、削减内容以及如何达成销售线索、收入和效率目标（CAC、回本周期、LTV 与 CAC 比例）
- 作为市场营销衡量的专家，向相关方解释因果关系、模型不确定性以及平台报告指标的局限性
增量性和实验
- 设计并运行持续进行的增量测试，包括用户级和地理级，以量化关键渠道、活动和策略的因果影响
- 计算增量提升、增量百分比和增量 ROAS/CPA，并据此指导预算重新分配
- 构建可重复使用的分析模板和操作手册，用于实验设计、分析和结果汇报，确保各团队结果一致
- 与渠道负责人（付费搜索、付费社交、生命周期、网站/SEO）合作，制定并优先排序实验路线图，将其嵌入到活动规划、创意测试和受众策略中
媒体组合建模与预测
- 使用历史数据构建和维护媒体组合模型，估算渠道贡献、边际回报和最优预算分配
- 结合季节性、广告衰减和饱和效应，持续优化模型
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ABOUT SUPABASE
Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth.
ABOUT THE ROLE
We're looking for a Senior Data Analyst, Marketing to join our Data Intelligence team and build the measurement foundation that tells us what's actually working across paid and PLG channels. You'll work closely with Marketing, Growth, and channel owners, helping us move past platform-reported metrics and vanity numbers into causal, trusted answers about what drives pipeline and revenue.
This role is ideal for someone who thrives in async, fast-paced environments, is AI-forward in how they work, and is excited about building a measurement function from the ground up.
WHAT YOU'LL BE RESPONSIBLE FOR
Marketing Measurement Strategy
- Own the end-to-end marketing measurement strategy across experimentation, media mix modeling, and attribution for paid and PLG channels
- Establish and evolve the attribution framework: how platform data, multi-touch attribution, MMM, and experiments work together to inform decisions
- Translate complex measurement outputs into clear recommendations on where to invest, what to cut, and how to hit pipeline, revenue, and efficiency targets (CAC, payback, LTV to CAC)
- Serve as the subject matter expert for marketing measurement, educating stakeholders on causality, model uncertainty, and the limitations of platform-reported metrics
Incrementality and Experimentation
- Design and run always-on incrementality tests, user-level and geo-level, to quantify the causal impact of key channels, campaigns, and tactics
- Calculate incremental lift, incrementality percent, and incremental ROAS/CPA, and use these to guide budget reallocation
- Build repeatable analysis templates and playbooks for experiment design, analysis, and readouts so results are consistent across teams
- Partner with channel owners (paid search, paid social, lifecycle, website/SEO) to build and prioritize a experimentation roadmap, embedding it into campaign planning, creative testing, and audience strategy
Media Mix Modeling and Forecasting
- Build and maintain media mix models using historical data to estimate channel contribution, marginal returns, and optimal budget allocation
- Incorporate seasonality, adstock, and saturation effects, and continuously validate model performance through backtesting and reconciliation with experiment results
- Turn MMM insights into budget scenarios and forecasts across channels and regions, communicated in a way non-technical stakeholders can act on
Context, Tooling, and Data Integrity
- Own and build the context and skills that let marketing teams run accurate self-serve analytics: metric definitions, model documentation, and reusable analysis patterns, not just dashboards
- Use AI tools as a core part of daily work to accelerate analysis and go deeper than a traditional analyst workflow allows, and help establish AI-forward practices across the marketing org
- Identify gaps and inconsistencies in marketing data (tracking, spend, platform exports) and work cross-functionally to fix them at the root rather than patching around them downstream
You Might Be a Good Fit If You
- Have 6+ years in marketing analytics, data science, or a related role, with a focus on performance marketing and/or PLG growth
- Deeply understand marketing attribution, incrementality testing, and media mix modeling, and how they complement each other rather than compete
- Are advanced in SQL and at least one statistical programming language (Python or R) for experiment analysis and modeling
- Have hands-on experience designing, running, and interpreting experiments across digital marketing channels: search, social, display, email, in-product
- Have built or worked closely with MMM and/or advanced attribution models, ideally in a consumption or BaaS environment
- Have strong business acumen and fluency in growth metrics: CAC, LTV, payback period, conversion rates, funnel performance
- Think in terms of self-service and scale: your instinct is to build tools and frameworks that make marketing teams independently capable, not to become the bottleneck for every "did this work" question
- Can hold technical and strategic context at once: you're as comfortable in a model's residuals as you are in a conversation about budget tradeoffs
- Are AI-forward in how you work: you use LLMs and AI tooling habitually and have a clear point of view on how it changes what an analyst can do
- Communicate clearly to non-technical stakeholders and know how to make causal nuance land in a business conversation
- Thrive in async, autonomous environments and are energized by building a measurement function from the ground up
Nice to Haves
- Experience in BaaS, DevRel or open source dev tool companies, connecting marketing spend to pipeline and revenue outcomes
- Experience with applied econometrics, time-series modeling, or Bayesian methods for MMM and experimentation
- Familiarity with common marketing and analytics tools (Google Ads, Meta, LinkedIn, web analytics, CDPs, BI/visualization tools)
WHAT WE OFFER
- Fully Remote
We hire globally. We believe you can do your best work from anywhere. There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world.
- ESOP
Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together.
- Tech Allowance
Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work.
- Health Benefits
Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us.
- Annual Off-Sites
Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year.
- Flexible Work
We operate asynchronously and trust you to manage your own time. You know what needs to be done and when.
- Professional Development
Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth.
ABOUT THE TEAM
Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love.
- ~400 team members
- 60+ countries
- 20+ languages spoken
- Over $1B raised (including our $500M Series F)
- 540,000+ community members
We move fast, build in public, and use what we ship. If it’s in your project, we probably use it in ours too. We believe deeply in the open-source ecosystem and strive to support—not replace—existing tools and communities.