Data Analytics Lead (Product Intelligence)

Job Description

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.

Qualifications

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.

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