Data Analyst SaaS

Jobgether; Netherlands; Remote; EUR 52,000 - 76,000; Full time

Job summary

Jobgether is seeking an experienced Data Analyst for a distributed international team working with multiple SaaS products in the Netherlands. You will transform complex data into actionable insights, analyzing acquisition, retention, revenue, and user journeys across channels and products.

You will collaborate with Data, Marketing, Product, and Finance teams, develop reusable analytics tools, evaluate experiments, and integrate AI-powered workflows to improve decision-making and business

Qualifications

• 3–5 years of professional experience as a Data Analyst or similar analytical role.

Responsibilities

• Analyze acquisition channels, marketing campaigns, and customer cohorts to evaluate costs, conversion and LTV.

Skills

SQL

Python

Cohort analysis

A/B testing

Data visualization

AI in analytics

Communication

Tools

Stripe

Adyen

Recurly

Chargebee

Power BI

Tableau

Looker

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Data Analyst SaaS based in the Netherlands. We are looking for an experienced Data Analyst to transform complex data into actionable insights and strategic business decisions across multiple subscription-based SaaS products. In this role, you will analyze the entire customer journey, from acquisition and user engagement to payments, revenue, and retention. You will work closely with Data, Marketing, Product, Finance, and executive teams to identify growth opportunities, uncover performance challenges, and improve business outcomes. Beyond traditional reporting and dashboard creation, you will investigate key business questions, develop analytical recommendations, and measure their impact. You will also contribute to building reusable data capabilities and integrating AI-powered tools into analytical workflows. This is an exciting opportunity to join a growing, fully distributed international team and have a direct impact on the performance and development of several global digital products.

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. 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.