Data Analytics Lead

Job Description

Data Analytics Lead (Product Intelligence) | Nexthink

The Tone:
This is a senior individual contributor role at Nexthink. Nexthink builds solutions that provide business intelligence to organizations, synthesizing various data sources to deliver product usage and business insights. This role is critical for shaping product strategy and decision-making by defining what the company’s data means, ensuring its trustworthiness, and transforming it into actionable insights.

The TL;DR
• Role: Full Time
• Location: Remote (with occasional travel to Lausanne, Switzerland)
• Team: Product Intelligence team
• Mission: Own the analytical and semantic layer of Product Intelligence, ensuring data trustworthiness and delivering insights that directly shape product strategy and decision-making.
• Tech Stack: SQL, Power BI, QuickSight, Tableau, dbt, Redshift, Snowflake, BigQuery, AWS (S3, Athena, Glue), Git, Claude Code (or similar agentic AI coding tools)

What You’ll Actually Do
• Insight & Analysis: Partner with Product Management and business stakeholders to translate product usage and telemetry data into product-led insights, identifying opportunities like upsell and churn prevention.
• Proactive Analysis: Perform self-directed analysis into product usage and user journeys, including funnels, feature adoption, and retention behavior, to uncover areas for product improvement.
• Data Definition & Governance: Own the semantic layer by defining core business entities and metrics, ensuring consistent understanding and application across the company for AI-readiness.
• Data Quality & Cataloging: Own data quality for the analytical layer, establishing expectations, monitoring for correctness failures, and maintaining a comprehensive data catalog for company-wide access.
• Data Collection & Engineering Partnership: Work with Product Managers and Engineering to define and implement new product telemetry, ensuring data collection aligns with business questions and supports necessary pipelines and models.

The Must-Haves
• Background: Senior individual contributor with a degree in a quantitative, technical, or business-analytical field, or equivalent practical experience.
• Experience: 7+ years in a data analyst, product analyst, analytics engineer, or business intelligence role, demonstrating high autonomy, experience partnering with Product and business teams for product-led insights, managing senior stakeholders, and presenting to executive audiences.
• Skills: Strong SQL, hands-on experience with product usage/telemetry data, ability to define success metrics, expertise in data visualization/BI tools (including semantic model building), solid understanding of data modeling concepts, experience with agentic AI coding tools, familiarity with the modern data stack, and excellent communication skills.
• Bonus: Practical experience owning data quality, experience with data cataloging or governance, solid understanding of AI/ML and LLM concepts regarding data trustworthiness, and an interest in using AI to enhance analytical workflows.