Data Product Manager

**Transform data into products that scale decision‑making**

Spendesk is seeking a Data Product Manager to join our data organization. You will own the vision, roadmap, and adoption of our internal data products: the analytics platform, core semantic models, and self‑service analytics experience for Spendeskers across Product, Finance, and Go‑to‑Market teams.

You’ll be the bridge between the “Datapoints” squad, product squads, and business stakeholders. Your mission is to turn our data stack into a reliable, intuitive product that powers decision‑making at scale.

This role is ideal for a product manager or data product owner who is comfortable with SQL, enjoys simplifying complexity, and excels at driving adoption and change across a whole company.

**About the role**

As a Data Product Manager, you will own the lifecycle of our internal data products, from discovery and strategy to delivery and rollout. You will:

- Define and drive the strategy for our analytics platform (including the migration from Looker/Metabase to a new, AI-native BI tool) and our core semantic data layer.
- Work hand‑in‑hand with the Datapoints squad (Data Engineering, Analytics Engineering, Software Engineering, Team Lead/EM) to turn this strategy into well‑scoped initiatives and shipped outcomes.
- Act as the “coach” for data product practices across Spendesk, helping product squads and business units (FP&A, RevOps, etc.) become autonomous and data‑savvy.
- Ensure that our data products solve real problems, are discoverable and well‑documented, and are actually used in day‑to‑day decisions.

Over the next 6-12 months, you’ll focus on tool consolidation, dashboard trimming, standard models and definitions, and a scalable self‑service analytics setup owned by domains rather than only the transversal data team.

**Our tech environment**

Our data platform relies on a modern data stack:

- Data platform: dbt (core and Cloud), Snowflake, Looker (original), Metabase, Amplitude for Product analytics
- Ingestion / exposure: Airbyte (cloud), Fivetran, Hightouch, Segment, Airflow
- Monitoring & collaboration: Github for versioning and CI/CD, Synq for observability, Notion for documentation, Slack for communication

**Key responsibilities**

**Product strategy & vision**

You will:

- Co‑lead (with the squad’s Team Lead & A Engs) the vision and roadmap for Spendesk’s internal data products: analytics platform, semantic layer, shared business models, and self‑service experience.- Align stakeholders (Head of Data, Product, FP&A, RevOps, etc.) on a clear 6-12 month plan, especially:

- Migrating analytics from Looker/Metabase to the new BI tool.
- Simplifying, standardizing and documenting (as code) our data models by focussing on the bronze layer of the DWH (raw, comprehensive)
- Enabling domain teams to own their metrics and analytics.
- Turn strategic objectives into concrete initiatives with clear problem statements, success metrics, and business impact.

**Analytics platform migration & adoption**

You will:

- Co‑lead (with the EM & AE) the migration from Looker and Metabase to our new BI tool:

- Inventory and rationalize existing dashboards and sources; decide what to migrate, merge, or decommission.
- Define migration priorities and criteria for key reporting.
- Own company‑wide adoption of the new BI tool:

- Design and run onboarding, training, and office hours.
- Define “golden paths” for building charts, dashboards, and semantic models.
- Monitor and act on adoption metrics (active users, usage of key dashboards, self‑service vs ad‑hoc).

**Data modeling & semantic layer (mentoring of 2 Analytics Engineers in your squad)**

You will:

- Help recruit and onboard 2 Analytics Engineers
- Partner with the Analytics Engineer on the roadmap for our core semantic layer on top of the DWH:

- Harmonize entities and naming across upstream systems.
- Define and maintain shared models and metrics (SSOT) for domains like customers, subscriptions, revenue, spend.
- Support semantic definitions as code (YAML or equivalent) and documentation.- Work with product squads and BUs to:

- Capture their analytics needs and translate them into reusable shared models.
- Ensure local models extend the shared layer rather than creating conflicting versions.

**Enablement & “data‑savviness” across squads and BUs**

You will:

- Act as internal coach for data‑savvy practices in product squads and BUs:

- Identify and nurture “data champions” (FP&A, RevOps, product) who own local analytics and dbt/BI usage.
- Design enablement tracks, from self‑service basics to advanced modeling and PR reviews.- Create and maintain guidelines for:

- Building high‑quality models and dashboards (naming, testing, performance, documentation).
- Contributing to shared models (ownership, PR etiquette, review process).
- Participate in PR reviews (e.g. dbt models, semantic definitions) to ensure consistency, while keeping ownership in squads/BUs.

**Product delivery & squad ritual