VP of Product Data

Salary- up to £110k base + 17% bonus + benefits

Work from Home (anywhere from the UK)

Our client is a global company looking for a Head of Product Data to define how product telemetry is captured, modelled, governed, and turned into data products that the rest of the business can trust and act on. This goes beyond product analytics. You'll own the telemetry platform strategy, establish shared data models that connect product usage across a multi-product portfolio, and build the data foundation for customer-facing insights that position usage transparency and outcomes measurement as a core part of our product offering.

This role sits within the Enterprise Data team. The data products you build will follow the same governance model, contracts, and quality standards as every other data product the team ships. You'll partner closely with the Data Engineering Lead on platform standards and pipeline design, with the Data & Analytics Lead on consumption and decision use cases, and with Product and Engineering leadership on instrumentation and roadmap alignment.

Reporting to the VP of Data, you'll have direct ownership over early design and implementation, with the opportunity to scale a product data function based on measurable impact.

Essential Criteria

1.     5+ years in a senior product data, product analytics, or analytics engineering role within a SaaS or technology-led organisation.

2.     Proven experience defining product instrumentation standards and telemetry architecture — you've designed event schemas, worked with engineering teams to implement tracking, and dealt with the messy reality of getting clean, consistent product data at scale.

3.     Experience building or owning a first-party telemetry or event pipeline — you understand the trade-offs between third-party analytics tools and owning your own event infrastructure, and you've been involved in designing or migrating to a platform-owned approach.

4.     Strong experience partnering with Product and Engineering teams to define success metrics, instrumentation requirements, and data-informed decision-making.

5.     Hands-on experience with modern data platforms (Databricks, Snowflake, BigQuery, or equivalent) and a strong understanding of how product data fits within a broader lakehouse or warehouse architecture.

6.     Strong SQL and Python skills, with the ability to work across the full stack from pipeline design to analysis.

7.     Experience building and leading small, high-performing technical teams — with the ability to translate ambiguous product questions into clear, actionable data work.

Desirable Criteria

1.     Experience with product analytics tooling such as Amplitude, Gainsight PX, Pendo, or similar — and a considered view on where dedicated product analytics tools add value vs. where a well-governed lakehouse can serve the same purpose.

2.     Experience with AI/ML techniques applied to product or behavioral data — such as predictive modelling, clustering, behavioral segmentation, or automated anomaly detection. Familiarity with Databricks' ML capabilities (MLflow, Unity Catalog, Feature Store) is a plus.

3.     Experience designing or delivering customer-facing analytics, usage dashboards, or outcomes reporting within a SaaS product.

4.     Experience building shared data models or normalisation layers across a multi-product portfolio — particularly where products have been acquired and data structures differ.

5.     Experience designing and shipping internal data products (APIs, curated datasets, or feature stores) for consumption by other teams.