Data Analyst SaaS

Accountabilities:

Analyze acquisition channels, marketing campaigns, and customer cohorts to evaluate acquisition costs, conversion rates, revenue generation, and customer lifetime value (LTV).
Identify the marketing channels, campaigns, and customer segments that generate the most profitable growth, providing actionable recommendations to Marketing teams and senior management.
Analyze the complete customer journey, from the first website visit and registration to free trials, subscriptions, and long-term retention.
Investigate conversion funnels, customer cohorts, user interactions, retention rates, and churn to identify friction points and opportunities to improve the customer experience.
Collaborate with Data Science specialists to conduct deeper analyses, evaluate experiments, and measure the impact of product and marketing initiatives.
Monitor payment performance, including transaction acceptance rates, declined payments, and retry mechanisms across payment service providers (PSPs), products, countries, and customer segments.
Analyze revenue performance and risk indicators, including payment disputes and fraudulent transactions, identifying unusual trends and areas requiring further investigation.
Work closely with Payment teams to recommend improvements to payment processes and evaluate the effectiveness of implemented initiatives.
Translate business questions from different departments and senior stakeholders into reliable, structured, and meaningful data analyses.
Collaborate with Data Engineering specialists to validate data quality, ensure consistency across multiple sources, and contribute to standardized business metric definitions.
Present analytical findings, explain limitations, and communicate clear, prioritized recommendations supported by relevant data.
Develop and maintain reusable data analysis tools, skills, and workflows that enable teams to explore information and answer business questions more independently.
Integrate AI-powered assistants and modern analytical technologies into daily workflows to improve the efficiency, accessibility, and quality of data analysis.
Requirements:
Between 3 and 5 years of professional experience as a Data Analyst, Business Intelligence Analyst, or in a similar analytical position.
Previous experience working with SaaS products, subscription-based business models, or e-commerce businesses, with a strong understanding of revenue generation and growth drivers.
Experience analyzing customer, marketing, product, or payment data, with the ability to explore unfamiliar business areas and understand their underlying metrics.
Strong SQL skills, including experience with joins, data segmentation, cohort analysis, and window functions.
Solid foundations in data analysis and statistics, including performance comparisons, cohort analysis, and interpretation of experimental results.
An AI-native mindset, with practical experience using AI assistants such as Claude or equivalent tools, cloud-based technologies, and reusable analytical workflows.
Ability to integrate AI into data exploration, analysis, and reporting processes while critically evaluating the accuracy and reliability of AI-generated results.
Strong understanding of key SaaS business metrics, including conversion rates, customer acquisition costs (CAC), retention, average revenue per account (ARPA), and customer lifetime value (LTV).
Ability to combine and analyze data from multiple sources while identifying inconsistencies and ensuring data reliability.
Familiarity with payment service provider data and user interaction analytics is an advantage, although specialist expertise in every area is not required.
Experience with Python, product analytics platforms, or business intelligence tools is considered a plus.
Familiarity with payment platforms such as Stripe or Adyen and subscription billing solutions such as Recurly or Chargebee is an additional advantage.
Strong business curiosity and analytical thinking, with the ability to investigate the reasons behind performance trends and understand their commercial implications.
Excellent critical thinking and attention to detail, including the ability to challenge assumptions, identify data limitations, and ensure analytical accuracy.
Ability to formulate, communicate, and defend clear business recommendations supported by quantitative evidence.
Strong autonomy, initiative, and prioritization skills, with the ability to manage multiple projects and collaborate across different departments.
Excellent communication and presentation skills, with the ability to explain complex analytical findings to both technical and non-technical stakeholders.
Fluency in French is required, as it is the primary business language.
English proficiency at B2 level or higher is required, as English is the secondary working language and will be assessed during the recruitment process.
Benefits:
100% remote working across Europe, with the flexibility to work from any European country.
Flexible working hours, allowing employees to organize their schedules according to their personal preferences and professional responsibilities.
Opportunity to work remotely from a different time zone for up to one month.
Two company-wide team seminars per year, organized in spring and autumn, offering opportunities for collaboration, connection, and team-building.
Professional development opportunities in a rapidly growing business, with the possibility of expanding responsibilities and influencing the company's future direction.
Exposure to multiple SaaS products and business models, providing opportunities to develop expertise across marketing, product analytics, payments, and finance.
Access to a modern data infrastructure, supported by dedicated Data Engineering expertise.
Direct collaboration with senior leadership and cross-functional teams, offering meaningful visibility and influence over business decisions.
An innovative and performance-oriented environment that encourages ownership, autonomy, continuous learning, and experimentation.
For employees based in Lausanne: access to modern office facilities, sports amenities, parking, and financial contributions toward commuting expenses.
An inclusive and collaborative remote culture built around trust, flexibility, accountability, and professional growth.