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Principal, Product Analytics
Это стратегическая позиция в быстрорастущей компании с реальными данными и сложным продуктом. Возможность влиять на архитектуру платформы следующего поколения и широкие полномочия делают вакансию крайне привлекательной для опытных аналитиков.
Сложность вакансии
Роль уровня Principal предполагает высокую степень ответственности за архитектуру данных и стратегию. Кандидату потребуется не только глубокая техническая экспертиза в Segment и Snowflake, но и способность выстраивать процессы с нуля в условиях неопределенности.
Анализ зарплаты
Учитывая уровень Principal и удаленный формат работы на глобальном рынке (США/Европа), ожидаемая зарплата значительно выше средней по рынку РФ. Предлагаемый диапазон соответствует топовым технологическим компаниям США для сотрудников с опытом более 8 лет.
Сопроводительное письмо
I am writing to express my strong interest in the Principal, Product Analytics position at Material Bank. With over 8 years of experience in building analytics functions from the ground up, I am particularly excited about the opportunity to lead the instrumentation and measurement strategy for the MB3 platform. My background in designing complex event taxonomies and attribution models aligns perfectly with your mission to provide better visibility for brand partners and internal stakeholders.
In my previous roles, I have successfully bridged the gap between Product, Marketing, and Data Engineering to ensure that data is not just collected, but used to drive strategic decisions. I have extensive experience with Segment, Snowflake, and dbt, and I thrive in ambiguous environments where I can establish scalable standards and move beyond surface-level metrics. I am eager to bring my expertise in marketplace dynamics and offline-to-online attribution to Material Bank to help the business understand its performance drivers at a deeper level.
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Откликнитесь в materialbank уже сейчас
Станьте ключевым архитектором данных в Material Bank и определите будущее аналитики для крупнейшего маркетплейса в индустрии дизайна!
Описание вакансии
Material Bank is the world’s largest material marketplace for the architecture and design industry. Operating in 37 countries, our platform has become the standard for design professionals around the globe. Every day, Material Bank connects thousands of designers with tens of thousands of materials from leading brands. Material Bank is the fastest and most powerful way for design professionals to search, sample, and specify materials.
We’re looking for a Principal, Product Analytics to build and lead analytics at Material Bank. This is a foundational role with broad ownership across how the business measures performance, makes decisions, and uses data day to day. You will join at an important moment as we launch MB3, our next-generation platform, a ground-up rebuild designed to make Material Bank faster, smarter, and more valuable for every stakeholder.
Material Bank has real, high-quality data to work with. Millions of sample requests, a highly engaged professional user base, and strong behavioral signals provide a clear view into how the business operates. At the same time, brand partners are looking for better visibility and attribution into performance. This role will shape how that data is structured, understood, and used across the company.
You will work across Product, Marketing, and the broader business to define what gets measured and how it drives decisions. Partnering closely with Data Engineering, you will establish the instrumentation, event taxonomy, and data models that make reliable analytics possible. There is a significant amount to build, and you will be expected to bring structure, ask better questions, and move beyond surface-level metrics to understand what is actually driving performance. The right person is comfortable operating in ambiguity, takes ownership of defining the problem as much as solving it, and uses data to challenge assumptions and drive better outcomes across the business.
What you’ll do
- Own and implement the end-to-end instrumentation plan for MB3, including event taxonomy, tracking standards, and data quality across the platform
- Establish core metrics for Product, Marketing, and leadership, ensuring alignment to business goals such as engagement, conversion, attribution, and platform health
- Define measurable success criteria, build and maintain the migration scorecard, and support stage gate decisions with clear data on adoption, performance, and user behavior
- Identify performance gaps, uncover opportunities, and size the impact of product initiatives to inform prioritization and roadmap decisions
- Write detailed dashboard requirements by stakeholder, partner with Data Engineering to ensure data models support reporting needs, and validate outputs for accuracy and usability
- Build and refine attribution approaches across digital and physical touchpoints, improving visibility into performance for internal teams and brand partners
- Partner closely with Product, Marketing, and Data to translate business questions into measurement plans and ensure insights lead to action
- Audit existing data systems (Segment, Snowflake), close gaps, and establish scalable data standards
What you’ll bring
- 8+ years in analytics with ownership of an analytics function or major initiative
- Strong experience with instrumentation and event taxonomy design; Segment preferred
- Experience building and applying attribution models, with a clear understanding of tradeoffs and limitations
- Ability to translate business questions into data requirements and challenge assumptions when needed
- Experience sizing business impact in terms of revenue, retention, or efficiency
- Experience defining KPIs and dashboard requirements across Product, Marketing, and leadership
- Ability to build structure from scratch in ambiguous environments
- Experience working with BI tools (Looker, Tableau, or similar) for requirements and QA
- Familiarity with Snowflake and dbt-based data models preferred
- Experience in marketplace, e-commerce, or B2B environments with complex user journeys preferred
- Experience with offline-to-online attribution (QR, NFC, or similar) preferred
*What you’ll get from us:*
- *Our people*: We are a growth-driven team that values efficiency, builds smart automation, operates in small empowered teams, and moves quickly from idea to execution.
- *Relaxation and Celebrations*: Flexible PTO, Sick Days, Paid National Holidays, and even more (ask us about this when we connect).
- *Health Benefits**: Wecontributeto your medical, dental, vision and short-term/long-termdisability plansand have a strong employee assistance program.*
- *Plan for your Retirement**: 401(k)eligibleafter your first 90 day's employed!*
- *Giving Back*: We sponsor multiple events throughout the year to help out our communities.
- *Growth*: We’ll help you take your career to the next level. We want you to be creative and take initiative which will allow you to grow and create within the company. Most importantly, be the best at what matters!
- *Flexible Work Schedules: With business units and employees across the globe, Material Technologies has embraced a hybrid working* model allowing department leaders to decide on the best approach for their respective teams, whether that be remote, in person, or a little of both.
Material Bank is proud to be an equal opportunity employer. We value diversity, and all applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, age, national origin, veteran or disability status or other status protected under any applicable federal, state or local law.
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Навыки
- Segment
- Snowflake
- dbt
- Looker
- Tableau
- Product Analytics
- Event Taxonomy
- Attribution Modeling
- SQL
- Data Modeling
Возможные вопросы на собеседовании
Проверка опыта проектирования систем сбора данных для нового продукта.
Как бы вы подошли к разработке таксономии событий для запуска MB3, чтобы обеспечить баланс между детализацией данных и производительностью системы?
Оценка навыков работы со сложными путями пользователя.
Расскажите о вашем опыте внедрения моделей атрибуции, объединяющих цифровые и физические точки касания (offline-to-online). С какими основными трудностями вы столкнулись?
Проверка умения влиять на бизнес-решения.
Приведите пример случая, когда ваш анализ данных заставил компанию изменить приоритеты в дорожной карте продукта. Как вы аргументировали свою позицию?
Оценка технического взаимодействия с инженерами.
Как вы выстраиваете процесс взаимодействия с командой Data Engineering для обеспечения качества данных и соответствия моделей dbt требованиям бизнес-отчетности?
Проверка лидерских качеств в условиях неопределенности.
Как вы определяете успех аналитической функции на этапе 'foundational role', когда многие процессы еще не созданы? Какие KPI вы бы установили для себя на первые 6 месяцев?
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