Analytics Engineer - Product Intelligence

Pleo's Intelligence function covers the full analytical picture - product behaviour, GTM performance, commercial data science, financial reporting, and operational intelligence. The Analytics Engineers who serve these teams own the data modelling layer that all of it runs on: dbt models , metric definitions, and semantic layer contributions that analysts, product teams, and AI tools depend on to get consistent, trustworthy answers.

You'll be embedded in the Product Intelligence team, building the tracking, experimentation, and data modelling foundations that make product data trustworthy and self-serve. You'll join a close-knit team actively migrating our analytics stack onto a new Analytics Warehouse and Omni , moving off legacy tools like Looker and Hippocampus . If you want to build the infrastructure that other people's product decisions run on rather than just report on what exists, this is the opportunity for you.

Who you'll work with and reporting to

You will report to an Analytics Manager leading Product Intelligence and Growth Intelligence. You'll partner closely with Product Managers, Product Analysts, and Engineering to get tracking, experimentation, and metric definitions right from the ground up. You'll also engage with the broader Analytics Engineering community across Pleo to drive shared standards, especially around the semantic layer that keeps metrics consistent across the organization.

What you'll be doing

• Build and maintain dbt models in our new Analytics Warehouse, using a clean, layered architecture and migrating logic off legacy tools as you go.
• Own tracking plan implementation and QA directly in Segment and Amplitude in close partnership with Product and Engineering.
• Support experimentation by modelling assignment and outcome data into structured formats that analysts and PMs can query directly.
• Define and document metrics in our semantic layer, ensuring a single authoritative definition so downstream BI tools and AI applications yield consistent answers.
• Apply AI-augmented data modelling practices as a standard part of how you write, review, and migrate code.
• Partner with Product Managers and Analysts to turn one-off questions into durable, reusable data models.
• Maintain data quality in your domain through dbt tests , freshness SLAs, and proactive monitoring.

What you bring

• Solid SQL and dbt experience, with a clear comfort owning models end to end.
• Working knowledge of event-based tracking tools like Segment or Amplitude .
• Experience with Git-based development workflows , including opening and reviewing pull requests.
• Clear communication skills to explain data definitions and structures to both technical and non-technical stakeholders.
• Genuine day-to-day use of AI tooling as part of your standard coding and workflow routine.
• Comfort navigating ambiguity and untangling legacy logic to build modern foundations.

This role is NOT a good fit if

• You only want to write dashboards without touching or owning the underlying data models.
• You require rigid processes and heavy upfront structure before taking initiative.
• You prefer to avoid data migration, cleanup work, and legacy system refactoring.

Your first 6 months

• Ramp up: Master our dbt project structure, tracking plans, and migration roadmap while shipping small, reviewed code changes early on.
• Take ownership: Own a dedicated slice of the migration, such as an event domain or our core experimentation data models.
• Drive consistency: Establish semantic layer definitions for your domain so analysts and PMs can self-serve accurate metrics without custom workarounds.

Our tech stack context

• Data warehouse: GCP / BigQuery (new Analytics Warehouse)
• Transformation: dbt
• Orchestration: Airflow
• Tracking and Product Analytics: Segment , Amplitude
• BI and Analytics: Omni (migrating off legacy Looker and Hippocampus )
• Languages: SQL