अय्यूब
भागीदारभोजनात्
Staff Analytics Engineer
अनुरोधेन मूल्यम्
विवरणम्
- रोजगार प्रकार
- पूर्णकालिक
- दूरस्थः
- न
- कम्पनी
- Pleo
वर्णनम्
About Pleo
Messy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. At Pleo, we're changing that. 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’.
The word ‘Pleo’ actually means ‘more than you’d expect’, and living by that mantra has been the secret to our success over the last 10 years.
Now, we’re at a pivotal moment in our journey; every move we make has a direct impact on our 40,000+ customers, our business, and our collective success. We need people who take pride in uncovering customer needs, who turn complex problems into simple solutions, challenge the way things are done (respectfully), and always aim high. With great ambitions driving us forward, we can’t say we’ve got this whole thing figured out. And frankly, that’s half the fun! What we can say is that we’re a driven, progressive, and, importantly, a kind bunch of 850+ people from over 100 nationalities, all committed to delivering the future of business spending, together.
About the role
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. This means that you won't own a domain but you'll own what good looks like across all of them by developing, improving , maintaining and evangelising our modelling standards and AI-augmented development practices that every Analytics Engineer work with, regardless of which team they sit in.
The semantic layer you will be designing and maintaining will be the single source of truth that AI agents, BI tools, and analysts query. This is foundational work with company-wide reach which will be ideal for you if you enjoy building things from the ground up. Our semantic layer is still in very early stage. Tooling selection is live, and this role has a strong voice in it. Our BI stack is also in transition so, you would not be inheriting a mature setup and maintaining it. You'd be deciding what it should be, then building it.
For additional context, our tech stack currently include: GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot.
Who you'll work with
You'll report to the Data Engineering Manager who oversees the Data Infra & MLOps team as well as the Data Services & Governance team. Your primary relationship will be with Analytics Engineers embedded across the Intelligence function who should come to you for architecture guidance, semantic layer decisions, and standards questions. You will also partner with a Staff Data Engineer on data engineering standards and pipeline practices, and with others on self-serve analytics and BI tooling governance. You'll engage with the Data Serving team on entity definitions and with the GenAI Platform team on what AI-ready data looks like at the platform boundary.
What you'll be doing
• 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. You make that real, not aspirational.
• 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 (not as a demonstration, but as your actual workflow). 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.
What you bring
• 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.…
Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/pleo/staff-analytics-engineer-united-kingdom-196102)
Messy spend management is tricky business. And tedious processes are a lose-lose situation for all involved, not just finance. At Pleo, we're changing that. 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’.
The word ‘Pleo’ actually means ‘more than you’d expect’, and living by that mantra has been the secret to our success over the last 10 years.
Now, we’re at a pivotal moment in our journey; every move we make has a direct impact on our 40,000+ customers, our business, and our collective success. We need people who take pride in uncovering customer needs, who turn complex problems into simple solutions, challenge the way things are done (respectfully), and always aim high. With great ambitions driving us forward, we can’t say we’ve got this whole thing figured out. And frankly, that’s half the fun! What we can say is that we’re a driven, progressive, and, importantly, a kind bunch of 850+ people from over 100 nationalities, all committed to delivering the future of business spending, together.
About the role
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. This means that you won't own a domain but you'll own what good looks like across all of them by developing, improving , maintaining and evangelising our modelling standards and AI-augmented development practices that every Analytics Engineer work with, regardless of which team they sit in.
The semantic layer you will be designing and maintaining will be the single source of truth that AI agents, BI tools, and analysts query. This is foundational work with company-wide reach which will be ideal for you if you enjoy building things from the ground up. Our semantic layer is still in very early stage. Tooling selection is live, and this role has a strong voice in it. Our BI stack is also in transition so, you would not be inheriting a mature setup and maintaining it. You'd be deciding what it should be, then building it.
For additional context, our tech stack currently include: GCP, BigQuery, dbt Core, Airflow, SQL, Python, Claude Code, GitHub Copilot.
Who you'll work with
You'll report to the Data Engineering Manager who oversees the Data Infra & MLOps team as well as the Data Services & Governance team. Your primary relationship will be with Analytics Engineers embedded across the Intelligence function who should come to you for architecture guidance, semantic layer decisions, and standards questions. You will also partner with a Staff Data Engineer on data engineering standards and pipeline practices, and with others on self-serve analytics and BI tooling governance. You'll engage with the Data Serving team on entity definitions and with the GenAI Platform team on what AI-ready data looks like at the platform boundary.
What you'll be doing
• 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. You make that real, not aspirational.
• 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 (not as a demonstration, but as your actual workflow). 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.
What you bring
• 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.…
Source: Arbeitnow (https://www.arbeitnow.co.uk/jobs/companies/pleo/staff-analytics-engineer-united-kingdom-196102)
इयं सूचीः भागीदार-फीड्-तः आगच्छति । स्रोतजालस्थले आवेदनं कुर्वन्तु।
स्रोतः: Pleo
Pleo द्वारा प्रदत्तं सूचीकरणम्।
