Staff Analytics Engineer Staff

D8 LooP; United Kingdom; Remote; GBP 70.000 - 110.000; Vollzeit

Zusammenfassung

D8 LooP is seeking a senior data specialist to shape the semantic layer and analytics standards for its spend-management platform. You will define data modelling approaches, govern the data contracts, and lead AI-assisted migrations across the analytics stack.

Working with backend and product teams, you will ensure scalable, cost-efficient data architectures and a single source of truth for analysts and AI agents.

Qualifikationen

• Proficient in Python and SQL with a focus on performance and cost in analytics.
• Deep BigQuery and SQL expertise including performance considerations and architectures for complex domains.
• Deep dbt modelling practices and a clear view of modelling architecture across functions.
• Experience with the listed tech stack: GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot.
• Engage upstream with backend and product engineers to drive data contracts and schema ownership upstream.
• Design and maintain the semantic layer as the single source of truth queried by AI agents, BI tools and analysts.
• Cost and performance optimization for data warehousing and migrations using AI tooling.

Aufgaben

• Set data modelling standards for the function across all domains and levels of complexity.
• Lead migrations and warehousing efforts with AI-assisted tooling to accelerate data modelling.
• Own the semantic layer and analytics standards within the Data Services & Governance team.
• Collaborate with backend and product engineers to enforce data contracts and schema ownership upstream.

Kenntnisse

Python

SQL

Tools

GCP

BigQuery

dbt Core

Airflow

Claude Code

GitHub Copilot

Jobbeschreibung

Messy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. We build spend solutions that make managing money seamless, empowering, and surprisingly effective for finance teams and employees alike - with a vision to help all businesses ‘go beyond’.

Skills, with evidence

• Python Our tech stack currently include: GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot. must have
• SQL Deep BigQuery and SQL expertise, including performance, cost considerations, and the architectural challenges of complex analytical domains. must have
• dbt 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. must have
• GCP Our tech stack currently include: GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot. must have · not practised here
• Airflow / orchestration Our BI stack is also in transition so, you would not be inheriting a mature setup and maintaining it.
• - Set data modelling standards for the function: what clean, layered, well-tested dbt architecture looks like across all domains, at all levels of complexity.
• Governance & security This is a senior individual contributor role in our Data Services & Governance team where you'll act as the thought and technical leader owning the semantic layer and analytics standards for Pleo. not practised here
• - Engage upstream with backend and product engineers to drive data contract discipline and schema ownership upstream, rather than managing inconsistency downstream.
• The semantic layer you will be designing and maintaining will be the single source of truth that AI agents, BI tools, and analysts query. not practised here
• Cost & performance - Deep BigQuery and SQL expertise, including performance, cost considerations, and the architectural challenges of complex analytical domains.
• Warehousing - Lead responsible AI-augmented migration work using AI tooling to accelerate data modelling and migrations across the Analytics Warehouse and Operational Data Platform.
• Machine learning You'll report to the Data Engineering Manager who oversees the Data Infra & MLOps team as well as the Data Services & Governance team. not practised here