Middle / Senior Product Analyst

Ориентир по рынку≈ 190 000 – 330 000 ₽наша приблизительная оценка по 24 вакансиям этой роли и грейда, у работодателя вилка не указана

🔄 ГибридMiddleПолная занятостьАнглийский C2

≈ 190 000 – 330 000 ₽наша приблизительная оценка

Стоит ли откликаться

Стоит откликнуться, но спросите про деньги

Сумму в объявлении не указали. Сколько платят за похожую работу, видно в разборе. Вакансия живая, задачи расписаны.

3 из 4 в норме

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Вакансия на английскомПереведёт заголовок и описание вакансии на русский

Чем предстоит заниматься

Xometry (NASDAQ: XMTR) powers the industries of today and tomorrow by connecting the people with big ideas to the manufacturers who can bring them to life. Xometry’s digital marketplace gives manufacturers the critical resources they need to grow their business while also making it easy for buyers at Fortune 1000 companies to tap into global manufacturing capacity

Product Analyst

Product Analyst is embedded within a product squad, providing analytical support to grow our two key marketplace platforms: the customer-facing platform and the partner-facing platform

The role involves deep analysis of user behavior, end-to-end analytics, and the planning and analysis of A/B tests. You will work in close collaboration with Product Managers to drive data-driven decisions, with a key focus on optimizing pricing strategies and identifying product growth opportunities

This position works end-to-end: clarifying problems, shaping metrics, partnering with DWH, building usable tools, and supporting adoption. The scope of the role is cross-regional

Наши требования

Education: Bachelor’s or Master’s in Data Science, Computer Science, IT, Engineering, or a related field

Experience: 3+ years in product analytics, ideally within B2B/B2C marketplaces

Technical Skills: Strong SQL and Python; experience with BI tools (Looker, Tableau, Qlik, or similar); familiarity with Git, JIRA, Confluence, or equivalents

Quantitative Skills: Solid foundations in probability and statistics; hands-on experience with statistical analysis and experimentation (hypothesis testing, A/B test design, power analysis), regression, and time-series analysis

Understanding of DWH concepts (design, data structures, ETL/ELT) and applied experience with Statsig or another A/B testing platform

Soft Skills: Strong business sense, clear communication, structured thinking, attention to data quality, and the ability to decompose ambiguous asks into deliverables

Curiosity: A track record of self-directed development in adjacent disciplines such as entrepreneurship, business administration, product, UX, machine learning, or human behavior and psychology is a strong plus

Languages: Fluent English with strong communication skills is required; Russian is a big plus; German or another EU language is a plus

Location: Munich, Germany

Please apply with an English CV

#Promoted

Analytics/DWH: SQL, Python, Snowflake, Airflow, Looker, Amplitude, Google Analytics, Google Sheets, DataHub, Git, Statsig, DBT

Application Platform: PostgreSQL, Ruby, Salesforce

Project/Process Management: JIRA, Miro, Figma

AI-Native: Claude, Spec-Driven Development

Дополнительно

Partner with Product Managers and stakeholders to define the problem, success metrics, and hypotheses; propose experiments and measure impact on the business

Own product A/B testing and the end-to-end product hypothesis pipeline, from ideation to analysis and implementation

Formulate hypotheses for new product features, design A/B experiments, analyze results, and provide recommendations to improve user conversion and engagement

Design and analyze A/B tests for different pricing models and strategies to maximize revenue while balancing conversion rate and bookings

Build and maintain dashboards in Looker; ensure correctness of data, clarity, usability, and decision support

Analyze product funnels, user behavior, and platform performance (activation, conversion, retention, pricing effects) to drive product growth, revenue, and margin

Track lifecycle KPIs, trends, and customer feedback, and recommend actions

Conduct in-depth product analysis and uncover insights not covered by self-service analytics

Specify data needs, validate data quality, and collaborate with DWH on marts and metrics

Treat analytics as a product: start with “why,” simplify, and ensure usability and adoption