Analytics Engineer: Data Modeling & Product Intelligence

Creandum; España; Hybrid; EUR 60,000 - 90,000; Full time

Benefits offered by this job

Pleo card

Lunch allowance

Private healthcare

25 days annual leave

Job summary

Pleo's Analytics Engineering team is building the data foundations that drive product decisions. You’ll own dbt models in the new Analytics Warehouse, migrating logic from legacy tools and defining a semantic layer so teams get trustworthy metrics.

You’ll collaborate with Product Managers and Engineers to implement tracking in Segment and Amplitude, support experiments, and push for AI-assisted data modelling as a standard practice across teams.

Qualifications

• Solid SQL and dbt experience; end-to-end model ownership.
• Event-based tracking tools such as Segment or Amplitude.
• Git-based development workflows including PRs.
• Clear communication of data definitions to diverse stakeholders.
• Daily use of AI tooling in coding and workflows.
• Ability to navigate ambiguity and untangle legacy logic to build modern foundations.

Responsibilities

• Build and maintain dbt models in the new Analytics Warehouse.
• Own tracking plan implementation and QA in Segment and Amplitude.
• Support experimentation by modelling data for analysis and queries.
• Define and document metrics in the semantic layer for consistent BI/AI outputs.
• Apply AI-augmented data modelling as a standard practice.
• Collaborate with Product Managers and Analysts to create reusable data models.
• Maintain data quality with dbt tests, freshness SLAs, and monitoring.

Skills

SQL

dbt

Data modelling

Segment

Amplitude

Git workflows

Communication

AI tooling

Tools

dbt

Segment

Amplitude

BigQuery

Job description

Pleo's Analytics Engineering team is building the data foundations that drive product decisions. You’ll own dbt models in the new Analytics Warehouse, migrating logic from legacy tools and defining a semantic layer so teams get trustworthy metrics.

You’ll collaborate with Product Managers and Engineers to implement tracking in Segment and Amplitude, support experiments, and push for AI-assisted data modelling as a standard practice across teams.