обновлено 9 дней назад
Revenue Operations Analyst (Data Analytics)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Revenue Operations Analyst (Data Analytics): Improving data usage across marketing, sales, customer success, product, and billing with an accent on defining metrics and investigating performance patterns. Focus on bridging the gap between raw data and business decisions through data modeling, automations, and critical analysis.
Location: Remote (Germany)
Company
focuses on improving the use of data across revenue-related business functions.
What you will do
- Analyse the customer journey across acquisition, conversion, retention, and revenue performance.
- Define and maintain consistent metrics across marketing, sales, customer success, product, and finance.
- Extract, combine, clean, and validate data from operational and analytical systems.
- Investigate customer behaviour, funnels, conversion rates, retention, costs, and revenue trends to identify performance drivers.
- Develop hypotheses, assess expected impact, prioritise opportunities, and present actionable recommendations.
- Support selected dashboards, data models, automations, process improvements, and measurement of implemented changes.
Requirements
- Strong analytical skills, structured problem-solving, and sound analytical judgement.
- High standards for data quality, accuracy, and reliable results.
- General programming knowledge and initial experience with SQL, Python, JavaScript, APIs, spreadsheets, data warehouses, or business intelligence platforms.
- Basic understanding of statistics, including the distinction between correlation and causation.
- Interest in AI-assisted analytical and technical workflows, with the ability to critically validate AI-generated results.
- Clear communication, curiosity, independence, persistence, and ownership of outcomes.
Culture & Benefits
- Freelance/contract assignment with fully remote work from Germany.
- Work is evaluated based on quality, reliability, and business impact rather than time spent online.
- No fixed list of reports; ownership is expected in questioning unclear results and improving analytical methods.
- Support is available while assigned areas are managed independently.
- Equal-opportunity selection based on skills, potential, and suitability for the assignment.
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