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Salary: EUR 62,000 - 110,000 / annual
Nexthink is seeking a Data Analytics Lead for the 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 also 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—identifying upsell opportunities, predicting churn, and supporting revenue targets.
- Perform proactive, self-directed analysis into product usage and user journeys, including funnel and drop-off analysis, feature adoption, cohort and retention behavior.
- Define, document, and own success metrics used to evaluate product features, releases, and business objectives.
- Provide regular reporting, insights, and recommendations to senior management.
- Manage senior stakeholders across the business, balancing competing analytical priorities.
**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, metric definitions, and metadata that enable AI and LLM-powered tools to query data reliably.
- Own data quality for the analytical layer, defining expectations and checks, monitoring for silent correctness failures.
- Build and maintain the data catalog and documentation so data consumers can find, understand, and trust data.
- Establish and maintain data governance practices for the analytics layer.
**Data Collection & Engineering Partnership:**
- Work with Product Managers and Engineering to define and implement new 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 your data engineering counterpart to deliver pipelines, models, and data structures.
- Contribute to the design and evolution of the analytics layer, including semantic modeling and dashboarding.
This is a deliberately broad, high-ownership analytics role rather than a report-building function. You will lead the analytics function, its standards, and its definitions, with influence coming from the quality of your work and partnerships across Product, Engineering, and the business.
**Requirements:**
- Degree in a quantitative, technical, or business-analytical field, or equivalent practical experience.
- 7+ years of experience in a data analyst, product analyst, analytics engineer, or business intelligence role, including operating with high autonomy and owning your own analytical agenda.
- Strong SQL skills—comfortable working directly in a data warehouse 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 and insights to executive/C-level audiences.
- Hands-on experience with product usage or telemetry data—funnels, user journeys, feature adoption, cohort and retention analysis.
- Experience defining success metrics and KPIs with judgment about metric design and behavioral incentives.
- Experience with data visualization and BI tools (Power BI, QuickSight, Tableau, or similar), including building the semantic/data model behind dashboards.
- 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 exploring data, writing and debugging queries, and automating analysis.
- Familiarity with the modern data stack—dbt, cloud data warehouses (Redshift, Snowflake, BigQuery), AWS services (S3, Athena, Glue)—and comfort using Git in an analytics workflow.
- Experience specifying telemetry or 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 their implications for data—why semantic definitions, metric consistency, and data quality determine trustworthy AI outputs.
- Ability to identify where AI can create value in analytics workflows.
- Curious and proactive; comfortable with ambiguity; collaborative; rigorous and honest about confidence levels and data limits.
- Available to travel to Lausanne headquarters on an occasional basis.
