Staff Analytics Engineer: Semantic Layer & AI-Driven Modeling

Pleo; Madrid; Presencial; EUR 90.000 - 120.000; Jornada completa

Descripción de la vacante

Pleo is seeking a senior Analytics Engineer to own the semantic layer and analytics standards across the organisation. You’ll shape modelling practices, data contracts, and AI-ready data schemas, collaborating with backend engineers, product teams, and the AE community.

The role emphasizes building from the ground up, guiding tool choices, and driving a governance-first analytics culture with AI-augmented development practices.

Formación

• Deep demonstrated expertise in dbt and modelling practices: not just strong modelling, but a formed view of what modelling architecture should look like across a function. You can defend trade-offs, teach them, and enforce them in teams you don't manage.
• Deep BigQuery and SQL expertise, including performance, cost considerations, and the architectural challenges of complex analytical domains.
• Real experience owning a semantic layer (LookML, dbt MetricFlow, or equivalent). You have a point of view on what metric consistency should look like at scale, what breaks when it doesn't, and how to design it so AI tooling can consume it without degrading trust.
• AI-native development practice in a data engineering context. You use AI coding tools (Claude Code, GitHub Copilot, or equivalent) as a genuine part of how you work, and you can be specific about where they add real value and where they introduce risk in a governed analytics codebase.
• A track record of setting standards across teams you don't directly manage and making them stick by leveraging effective influencing techniques as opposed to relying on authority.
• Understanding of what LLMs and agentic tools need from a data layer such as how to model data, write documentation, and define metrics so that AI tools get consistent answers at runtime.
• Proven experience engaging credibly with backend engineers on data contracts and with senior stakeholders on what the semantic layer strategy means for the business.

Responsabilidades

• Own the semantic layer: define what a metric definition is, how it's structured, where it lives, and how it's enforced. The goal is one canonical definition of Monthly recurring revenue (MRR), churn, transaction, customer - used by analysts, BI tools, and AI tools without divergence.
• Set data modelling standards for the function: what clean, layered, well-tested dbt architecture looks like across all domains, at all levels of complexity.
• Build and run the Analytics Engineering community of practice including code reviews, shared patterns, documentation, onboarding etc. The goal is to create a social infrastructure that makes standards stick.
• Own the AI-native development practice. Use AI coding tools as a native part of how you write, review, and migrate data models; evaluate what works in a governed data engineering context, what introduces risk, and shape how the Analytics Engineering community adopts it.
• Lead responsible AI-augmented migration work using AI tooling to accelerate data modelling and migrations across the Analytics Warehouse and Operational Data Platform.
• Design data models and metric definitions in a documented and structured manner enabling reliable consumption by AI services without human intervention.
• Engage upstream with backend and product engineers to drive data contract discipline and schema ownership upstream, rather than managing inconsistency downstream.
• Contribute to Data & AI Products for external customer-facing analytics where data modelling patterns and conversational analytics needs and capabilities grow.

Conocimientos

dbt

BigQuery

SQL

semantic layer

AI-native development

LookML

dbt MetricFlow

Claude Code

GitHub Copilot

data contracts

Herramientas

dbt Core

Airflow

LookML

MetricFlow

Descripción del empleo

Pleo is seeking a senior Analytics Engineer to own the semantic layer and analytics standards across the organisation. You’ll shape modelling practices, data contracts, and AI-ready data schemas, collaborating with backend engineers, product teams, and the AE community.

The role emphasizes building from the ground up, guiding tool choices, and driving a governance-first analytics culture with AI-augmented development practices.