Data Analytics Lead (Product Intelligence)

About the role

Nexthink, a digital employee experience management software company, is looking for a Data Analytics Lead to own the analytical and semantic layer of its Product Intelligence function. The role synthesizes data sources across the company to deliver product usage and business insights that shape product strategy, working closely with Product Management and Engineering. It is a senior individual contributor position with high ownership, reporting regularly to senior management.

Key responsibilities

• Turn product usage and telemetry data into insights such as upsell opportunities, churn prediction and revenue support, and run self-directed analysis of funnels, user journeys, feature adoption, cohorts and retention.
• Define, document and own the success metrics used to evaluate features, releases and business objectives.
• Present insights and recommendations to senior management and manage senior stakeholders across Product, Sales, Customer Success and Finance.
• Own the semantic layer: define consistent meanings for core entities and metrics, and champion an AI-ready data strategy with semantics, metadata and data quality checks.
• Build and maintain the data catalog and documentation, and set up data governance practices (ownership, lineage, definitions, access).
• Work with Product and Engineering to define telemetry events and properties, identify instrumentation gaps, and collaborate with data engineering on pipelines, models and data structures.
• Contribute to the analytics layer, including semantic modeling and dashboards in BI tools, and promote an agile, iterative way of working.

Required profile

• Degree in a quantitative, technical or business-analytical field, or equivalent practical experience.
• 7+ years in a data analyst, product analyst, analytics engineer or business intelligence role, with autonomy and ownership of an analytical agenda.
• Proven experience partnering with Product teams and presenting to executive/C-level audiences.
• Fluency in English; occasional travel to the Lausanne headquarters.

Required skills

• Strong SQL on large, multi-source data in a data warehouse.
• Data visualization and BI tools such as Power BI, QuickSight or Tableau, including the underlying data model.
• Understanding of data modeling (facts, dimensions, grain, slowly changing dimensions).
• Agentic AI coding tools such as Claude Code.
• Modern data stack: dbt, cloud data warehouses (Redshift, Snowflake, BigQuery), AWS (S3, Athena, Glue) and Git.