VP of Product Data

VP of Product Data

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

Work from Home (anywhere from the UK)

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.

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.