Data Analyst, SaaS
New
J
JobgetherSaaS analytics
100% remote working across Europe, with the flexibility to work from any European country., Opportunity to work remotely from a different time zone for up to one month.Full-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- Fluency in French is required, as it is the primary business language. English proficiency at B2 level or higher is required.
- Experience
- Between 3 and 5 years of professional experience as a Data Analyst, Business Intelligence Analyst, or in a similar analytical position.
- Required Skills
- SQLData Analysis
Requirements
- Have 3–5 years of experience as a Data Analyst, Business Intelligence Analyst, or in a similar analytical role.
- Have experience with SaaS products, subscription business models, or e-commerce and understand revenue generation and growth drivers.
- Have experience analyzing customer, marketing, product, or payment data.
- Have strong SQL skills, including joins, data segmentation, cohort analysis, and window functions.
- Have solid foundations in data analysis and statistics, including performance comparisons, cohort analysis, and interpretation of experimental results.
- Have practical experience using AI assistants such as Claude or equivalent tools, cloud-based technologies, and reusable analytical workflows.
- Be able to integrate AI into data exploration, analysis, and reporting while critically evaluating AI-generated results.
- Understand SaaS metrics including conversion rates, customer acquisition costs, retention, average revenue per account, and customer lifetime value.
- Be able to combine data from multiple sources, identify inconsistencies, and ensure data reliability.
- Be fluent in French and proficient in English at B2 level or higher.
- Experience with Python, product analytics or business intelligence tools, payment service provider data, Stripe, Adyen, Recurly, or Chargebee is advantageous.
Responsibilities
- Analyze acquisition channels, campaigns, and customer cohorts to assess acquisition costs, conversion rates, revenue, and customer lifetime value.
- Identify profitable marketing channels, campaigns, and customer segments and recommend actions to Marketing teams and senior management.
- Analyze the customer journey, conversion funnels, user interactions, retention, and churn to identify friction points and improvement opportunities.
- Collaborate with Data Science specialists on deeper analyses, experiments, and measurement of product and marketing initiatives.
- Monitor payment performance across payment service providers, products, countries, and customer segments.
- Analyze revenue performance, payment disputes, and fraudulent transactions to identify unusual trends.
- Work with Payment teams to recommend process improvements and evaluate implemented initiatives.
- Translate stakeholder questions into structured analyses, validate data quality with Data Engineering specialists, and contribute to standardized business metrics.
- Present findings, limitations, and prioritized recommendations supported by data.
- Develop reusable analysis tools and workflows and integrate AI assistants into analytical work.
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