Staff Analytics Engineer

Role overview

Staff Analytics Engineer at Pleo is a senior individual contributor role within the Data Services & Governance team. You will act as the thought and technical leader owning the semantic layer and analytics standards used across analytics engineering teams.

Responsibilities

• Own the semantic layer: define metric definitions (e.g., structure, location, enforcement) and ensure a single canonical source of truth across the org.
  • Example target: canonical definitions for Monthly Recurring Revenue (MRR), churn, transactions, and customers with no divergence across analysts, BI tools, and AI tools.
• Set analytics engineering data modelling standards across domains and complexity levels.
  • Define what a clean, layered, well-tested dbt Core architecture looks like.
• Build the Analytics Engineering community of practice
  • Lead code reviews, establish shared patterns, documentation, and onboarding so standards “stick.”
• Own AI-native development practice
  • Evangelise and improve AI-augmented development practices that analytics engineers use.
• Influence tooling and BI strategy (semantic layer is early stage)
  • Tooling selection is in progress; BI stack is transitioning—this role helps decide what the future stack should be and then builds it.

Collaboration

• Reports to the Data Engineering Manager overseeing Data Infra & MLOps and Data Services & Governance.
• Works closely with embedded Analytics Engineers for architecture, semantic layer decisions, and standards.
• Partners with a Staff Data Engineer on engineering standards and pipeline practices.
• Collaborates with teams across self-serve analytics/BI tooling governance, Data Serving (entity definitions), and the GenAI Platform team (AI-ready data at platform boundary).

Requirements

• Strong ability to design and govern a semantic layer as the single source of truth for metrics.
• Expertise in analytics engineering data modelling and defining enforceable standards.
• Experience with dbt Core modelling practices and testing.
• Capability to lead community-wide standards through reviews, documentation, and onboarding.

Nice-to-haves (implied by stack/positioning)

• Familiarity with building AI-augmented analytics engineering workflows using AI coding tools.
• Experience working with a transitioning BI stack and making architecture/tooling decisions.

Tech context (current stack)

• GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot