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Salary: EUR 62,000 - 110,000 / annual
Nexthink is seeking a Data Analytics Lead for its Product Intelligence team. This is a senior individual contributor role focused on owning the analytical and semantic layer of product intelligence—defining what data means, ensuring trustworthiness, and translating it into actionable insights that shape product strategy and business decisions.
You will work closely with Product Management and Engineering to define telemetry requirements, establish success metrics, and proactively investigate product usage and user journeys to surface opportunities. You'll be the analytical voice presenting insights and recommendations to senior leadership, managing competing priorities across Product, Sales, Customer Success, and Finance.
Key responsibilities include:
**Insight & Analysis:** Partner with stakeholders to translate product usage data into product-led insights (upsell opportunities, churn prediction, revenue support). Perform self-directed analysis on product usage, funnels, feature adoption, cohort and retention behavior. Define and own success metrics for features, releases, and business objectives. Provide regular reporting and insights to senior management. Manage senior stakeholders across the business.
**Data Definition, Quality & Governance:** Own the semantic layer by defining core business entities and metrics (customer, account, active user, adoption, churn, expansion) to ensure consistency across the company. Champion an AI-ready data strategy through semantic definitions and metadata. Own data quality for the analytical layer, including quality expectations, monitoring, and issue resolution. Build and maintain the data catalog and documentation. Establish data governance practices proportionate to a fast-moving team.
**Data Collection & Engineering Partnership:** Work with Product and Engineering to define and implement product telemetry, specifying events, properties, and grain needed to answer business questions. Identify gaps in current data collection and make the case for new instrumentation. Define business and data requirements clearly for engineering delivery. Contribute to analytics layer design and evolution. Promote agile, iterative working practices.
The role is based in Madrid with occasional travel to the Lausanne headquarters. Nexthink offers a hybrid work model, competitive compensation, private health insurance, meal vouchers, flexible hours, unlimited vacation (plus 23 days), gym subsidies, relocation support, and professional development reimbursement.
**Requirements:**
- Degree in quantitative, technical, or business-analytical field, or equivalent practical experience
- 7+ years in data analyst, product analyst, analytics engineer, or business intelligence role with high autonomy
- Strong SQL skills; comfortable working directly in data warehouses against large, messy, multi-source data
- Proven experience partnering with Product and business teams to deliver product-led insights (upsell, churn prediction, revenue growth)
- Demonstrated experience managing senior stakeholders and presenting data strategy to executive/C-level audiences
- Hands-on experience with product usage and telemetry data (funnels, user journeys, feature adoption, cohort and retention analysis)
- Experience defining success metrics and KPIs with judgment about behavioral incentives
- Experience with data visualization and BI tools (Power BI, QuickSight, Tableau, or similar), including semantic/data model design
- Solid understanding of data modeling concepts (facts, dimensions, grain, slowly changing dimensions)
- Hands-on experience using agentic AI coding tools (Claude Code or similar) for data exploration, query writing, debugging, and automation
- Familiarity with modern data stack (dbt, cloud data warehouses like Redshift, Snowflake, BigQuery, AWS services like S3, Athena, Glue) and Git
- Experience specifying telemetry and instrumentation requirements with Product and Engineering teams
- Excellent communication skills; ability to explain complex findings simply without losing rigor; fluency in English
- Practical experience owning data quality (checks, expectations, discrepancy investigation)
- Experience with data cataloging, documentation, or data governance practices
- Solid understanding of AI/ML and LLM concepts and implications for data (semantic definitions, metric consistency, data quality for trustworthy AI outputs)
- Ability to identify where AI creates value in analytics workflows
- Curious, proactive mindset; comfortable with ambiguity; collaborative; rigorous about confidence levels and data limitations
