About the role

The Product Intelligence team serves a critical role by providing business intelligence to the organization: we synthesize data sources across the company, delivering product usage and business insights that directly shape product strategy and decision-making.

We are looking for a Data Analytics Lead to own the analytical and semantic layer of Product Intelligence – the person who defines what our data means, makes sure it is trustworthy, and turns it into insight that changes decisions. You will work hand in hand with Product Management and Engineering to define the telemetry we need in order to answer the questions the business is actually asking, establish the success metrics that product and commercial teams are measured against, and proactively investigate product usage and user journeys to surface opportunities PMs would not have found on their own. You will also be the analytical voice in the room with senior leadership, regularly presenting insights and recommendations to senior management.

This is a deliberately broad, high-ownership analytics role rather than a report-building one. You will own data quality, cataloging, and the semantic definitions that make our data AI-ready, and you will partner closely with your data engineering counterpart – defining the business needs and data requirements, and collaborating with them to deliver the pipelines and models that support them.

As a senior individual contributor, you will lead our analytics function, its standards, and its definitions. As Lead you are expected to own the discipline and set its direction, with your influence coming from the quality of your work and your partnerships across Product, Engineering, and the business.

What you'll do

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 – funnel and drop-off analysis, feature adoption, cohort and retention behaviour – to help Product Managers understand how the product is really used and where to improve it.
Define, document, and own the success metrics used to evaluate product features, releases, and business objectives, and make sure they are consistently understood and applied across teams.
Provide regular reporting, insights, and recommendations to senior management, and present them directly to that audience.
Manage senior stakeholders across the business, balancing competing analytical priorities from Product, Sales, Customer Success, and Finance.

Data definition, quality & governance

Own the semantic layer: define what our core business entities and metrics mean – customer, account, active user, adoption, churn, expansion – so that a given metric means the same thing everywhere in the company.
Champion an AI-ready data strategy by defining the semantics, metric definitions, and metadata that allow AI and LLM-powered tools to query our data reliably and return trustworthy answers.
Own data quality for the analytical layer: define quality expectations and checks, monitor for silent correctness failures, and drive issues to resolution with the owning teams before they reach a dashboard or a leadership deck.
Build and maintain the data catalog and documentation, so that data consumers across the company can find data, understand its meaning, and know how much to trust it.
Establish and maintain data governance practices for the analytics layer – ownership, lineage, definitions, and access – proportionate to a fast-moving team.

Data collection & partnering with Engineering

Work with Product Managers and Engineering to define and implement new product telemetry, specifying the events, properties, and grain needed to answer real business questions – and validating that what ships actually answers them.
Identify gaps in what we currently collect, and make the case for the instrumentation needed to close them.
Define business and data requirements clearly enough to be built, and collaborate with your data engineering counterpart to deliver the pipelines, models, and data structures required to support them.
Contribute to the design and evolution of the analytics layer, including semantic modeling and dashboarding within our BI tools.
Promote an agile, iterative way of working and actively contribute to team ceremonies.

What we're looking for

Analytics experience

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 experience operating with a high level of autonomy and owning your own analytical agenda.
Strong SQL – comfortable working directly in a data warehouse against large, messy, multi-source data without needing someone to prepare it for you.
Proven experience partnering with Product and business teams to deliver product-led insights, such as identifying upsell opportunities, predicting churn, or supporting revenue growth.
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, and the judgement to know when a proposed metric will drive the wrong behaviour.
Experience with data visualization and BI tools such as Power BI, QuickSight, Tableau, or similar – including building the semantic/data model behind a dashboard, not just the dashboard itself.
Solid understanding of data modeling concepts (facts, dimensions, grain, slowly changing dimensions) and the ability to reason about whether a data model can answer a given business question.
Hands-on experience using agentic AI coding tools – Claude Code or similar – as part of your day-to-day analytical work: exploring unfamiliar data, writing and debugging queries and transformations, and automating repetitive analysis.
Familiarity with the modern data stack – dbt, cloud data warehouses (Redshift, Snowflake, BigQuery, or similar), AWS services such as S3, Athena, or Glue – and comfort using Git as part of an analytics workflow.
Experience specifying telemetry or instrumentation requirements together with Product and Engineering teams.
Excellent communication skills, with the ability to explain a complex finding simply without losing its rigor. Fluency in English.
Available to travel to our head office in Lausanne on an occasional basis.

Data quality, governance & AI-readiness

Practical experience owning data quality – defining checks and expectations, investigating discrepancies, and distinguishing a genuine business result from a data problem.
Experience with data cataloging, documentation, or data governance practices, and an understanding of why shared definitions matter more than tooling.
Solid understanding of AI/ML and LLM concepts and their implications for data – in particular, why semantic definitions, metric consistency, and data quality determine whether an AI tool gives a trustworthy answer or a plausible wrong one.
Ability to identify where AI can create value in an analytics workflow, and interest in using it to work faster.

How you work

Curious and proactive: you go looking for the insight nobody asked for, and you sanity-check a result against business reality before you present it.
Comfortable with ambiguity – able to turn a vague business question into a well-defined analysis, and to say clearly when the data cannot answer it.
Collaborative: you work as a genuine partner to Product Managers and to your data engineering counterpart rather than as a request queue.
Rigorous and honest about confidence levels, caveats, and the limits of the data.

What you'll get

We are the pioneers and trailblazers of a global IT Market Category (DEX) that is shaping the future of how the world works, giving our customers’ IT Teams total digital visibility across their enterprise. Our innovative solutions integrate real-time analytics, automation, and employee feedback across all endpoints. This enables our IT teams to solve complex technical challenges, create ever more productive workplaces, and deliver happy, satisfied employees in the digital workplace.

With over 1000 employees across 5 continents, Nexthink operates as One Team, connecting, collaborating and innovating to continuously grow. We call our employees ‘Nexthinkers’ and our commitment to diversity, inclusion, and equity is second to none. We currently have over 75 nationalities working with us, from all cultures and backgrounds, speaking many different languages.

IIf you are looking for a change and like a nice atmosphere, lots of challenges, and having fun while working, this is a great opportunity for you! Check what we offer:

Permanent Contract and a competitive compensation package.
📍 Amazing centrally located offices near the Bernabeu Stadium.
🩺 Private Health Insurance (Sanitas) and daily meal vouchers of 11 EUR will be entirely covered by us.
🏡 Hybrid work model balancing office and remote work, with a structured approach for new hires to foster connections and onboarding.
🏖️ Flexible Hours and unlimited vacation (employees have unlimited paid time off on top of the 23 days of holidays we offer) plus 3 company-paid volunteer days.
🤸 Up to 25 EUR per month for a gym subscription.
🛴 Flexible compensation plan for childcare & public transportation.
🧑‍🏫 Reimbursement of up to 50% of the cost of English & Spanish classes.
🍉 Fresh fruit, cookies, soft drinks and protein shakes at the offie.
🍕 Regular company and team events like Pizza talks, Team Building activities, Christmas parties, hosting Meetups at the office and more!
📣 Bonuses for referring successful hires after three months of continuous employment.
🚚 We offer a relocation package to people who are coming from another country.

Please note that not all the benefits listed above are available for temporary, contract, and internship roles. To ensure you have the most up-to-date information, we recommend checking with your Recruitment Partner.

The base salary for this role is €85,000 – €120,000 gross per year, with a total on-target earnings (OTE) range of €97,750– €140,000 including an annual performance bonus. You'll also be part of our broader total rewards package — including benefits tailored to where you live and how you work best.

We set our pay ranges using objective criteria: the scope and level of the role, the skills it takes to do it well, and the relevant market data. Ranges are reviewed every year to remain competitive and fair. We're transparent about this because we think you deserve to know what you're working towards from day one. In accordance with the EU Pay Transparency Directive (2023/970), we publish salary ranges on every Nexthink role. We won't ask what you currently earn or your previous salary. What matters to us is what this role is worth and whether it works for you. Nexthinkers come from all kinds of backgrounds, and that's what makes us stronger. We welcome applications from everyone.

About the company & team

Nexthink is the leader in digital employee experience management software. The company provides IT leaders with unprecedented insight allowing them to see, diagnose and fix issues at scale impacting employees anywhere, with any application or network, before employees notice the issue. As the first solution to allow IT to progress from reactive problem solving to proactive optimization, Nexthink enables its more than 1,300 customers to provide better digital experiences to more than 18 million employees. Dual headquartered in Lausanne, Switzerland and Boston, Massachusetts, Nexthink has 9 offices worldwide.

Data Analytics Lead (Product Intelligence)

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