Analytics Engineer: Product Data & Metrics

United States Digital Space LLC; London; Hybrid; NOK 840,000 - 1,213,000; Full time

Benefits offered by this job

Company card

Lunch/flexible meals

Private healthcare

25 days annual holiday

Hybrid/Remote options

Mental health support

Parental leave

Job summary

PleoMessy is modernising spend management with analytics at its core. You’ll join the Product Intelligence team to build trustworthy data foundations, migrate analytics to a new Analytics Warehouse, and move away from legacy tools like Looker.

You’ll own end-to-end data models and empower product decisions with self-serve analytics, in a collaborative, global team. Hybrid or remote options available with a strong focus on data quality.

Qualifications

• Strong SQL and dbt experience across end-to-end model ownership.
• Experience with event-based tracking in Segment or Amplitude.
• Experience with Git-based development workflows and PR reviews.
• Ability to explain data definitions to both technical and non-technical stakeholders.
• Experience with AI tooling in data projects.
• Comfort with ambiguity and legacy data refactoring.

Responsibilities

• Build and maintain dbt models in the new Analytics Warehouse.
• Own tracking plan implementation and QA in Segment/Amplitude.
• Model experimentation data for analysts and PMs.
• Define metrics in the semantic layer to ensure consistency.
• Apply AI-augmented data modeling practices.
• Collaborate with Product Managers to create reusable data models.
• Ensure data quality with dbt tests and monitoring.

Skills

SQL

dbt

Event tracking (Segment/Amplitude)

Git workflows

Communication

AI tooling

Ambiguity handling

Tools

Segment

Amplitude

BigQuery / Analytics Warehouse

Git

Job description

PleoMessy is modernising spend management with analytics at its core. You’ll join the Product Intelligence team to build trustworthy data foundations, migrate analytics to a new Analytics Warehouse, and move away from legacy tools like Looker.

You’ll own end-to-end data models and empower product decisions with self-serve analytics, in a collaborative, global team. Hybrid or remote options available with a strong focus on data quality.