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Salary: EUR 85,000 - 120,000 / annual
Nexthink is seeking a Data Analytics Lead for its Product Intelligence team, a critical function that synthesizes data across the organization to deliver product usage and business insights that shape product strategy and decision-making.
In this senior individual contributor role, you will own the analytical and semantic layer of Product Intelligence—defining what data means, ensuring its trustworthiness, and transforming it into actionable insights. 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 will serve as the analytical voice to senior leadership, regularly presenting insights and recommendations.
Key responsibilities include:
**Insight & Analysis:**
- Partner with Product Management and business stakeholders to translate product usage and telemetry data into product-led insights (upsell opportunities, churn prediction, revenue support)
- Perform proactive, self-directed analysis of product usage and user journeys (funnel analysis, feature adoption, cohort and retention behavior)
- Define, document, and own success metrics for product features, releases, and business objectives
- Provide regular reporting and insights to senior management with direct presentation to executive audiences
- Manage competing analytical priorities across Product, Sales, Customer Success, and Finance
**Data Definition, Quality & Governance:**
- Own the semantic layer: define core business entities and metrics (customer, account, active user, adoption, churn, expansion) for consistency across the company
- Champion an AI-ready data strategy through semantic definitions, metric definitions, and metadata that enable reliable AI/LLM-powered data queries
- Own data quality for the analytical layer: define expectations, monitor for silent failures, and drive resolution before dashboards or leadership presentations
- Build and maintain the data catalog and documentation for company-wide data discovery and understanding
- Establish data governance practices for the analytics layer (ownership, lineage, definitions, access)
**Data Collection & Engineering Partnership:**
- Work with Product Managers 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 needed instrumentation
- Define business and data requirements clearly for engineering delivery
- Collaborate with data engineering counterparts to deliver pipelines, models, and data structures
- Contribute to analytics layer design and evolution, including semantic modeling and BI dashboarding
- Promote agile, iterative working practices
This is a deliberately broad, high-ownership analytics role focused on defining standards and direction through the quality of work and cross-functional partnerships rather than report-building.
**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 and self-directed analytical agenda
- Strong SQL: 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 opportunities, churn prediction, revenue growth support)
- Demonstrated experience managing senior stakeholders and presenting data strategy/insights 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 metric-driven behavior
- Experience with data visualization and BI tools (Power BI, QuickSight, Tableau, or similar), including building semantic/data models
- 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 analysis automation
- Familiarity with modern data stack (dbt, cloud data warehouses like Redshift/Snowflake/BigQuery, AWS services like S3/Athena/Glue)
- Comfort using Git as part of analytics workflow
- 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 (defining checks, investigating discrepancies, distinguishing business results from data problems)
- 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 and proactive; comfortable with ambiguity; collaborative; rigorous about confidence levels and data limitations
- Available to travel to Lausanne headquarters occasionally
