Analytics Engineer (Central Data)

Senior Analytics Engineer (Central Data)

Central Data owns Finom's central data model, the certified metrics and the rails through which the company and its AI agents use data. As our Senior Analytics Engineer, you'll keep developing that model and build the tooling that connects it to the AI tools used across the company, so people and agents can get more out of the data. You'll work directly with business functions on their processes. You'll also use LLMs where they do the job better than code, such as classification, extraction and translation, evaluated like any other pipeline step.

This isn't a «build what's in the ticket» role. You'll work with stakeholders on how their processes run, suggest improvements and connect data work to business cost and value. You'll report to the Head of Central Data.

• Design and evolve entities, metrics and data products in the central model, with tests, lineage and documentation, so that their consumers can reuse and extend them
• Build and maintain pipelines, onboard new data sources on your own, keep CI/CD and data quality checks running, and take part in migrations
• Interview stakeholders, understand how their processes work and scope what they need. Connect each piece of data work to its business cost and value
• Take part in recurring reporting, including regulatory and audit cycles, reconciliations and data requests from business functions
• Build LLM steps into reporting and metric pipelines, such as translation, classification, basic aggregation and preparing unstructured data. Evaluating output quality is part of delivery
• Expose the semantic layer to internal tools and agents through MCP and APIs, working with engineering on shared standards
• Prepare data for agentic tools, company-wide AI harnesses and external recipients from the same certified definitions, so it is reproducible and traceable back to the model

In your first months you get to know the model, the stack and your stakeholders. By six months you're self-sufficient and make judgement calls on scope and design without escalating. From there, you set long-term goals together with your stakeholders, own a domain roadmap and mentor others on semantic-layer and analytics-engineering practice.

What This Role Is Not

• Not a business analyst role without technical depth
• Not an engineer who builds only what's in the ticket
• Not ML research or model training
• Not platform infrastructure, which belongs to our Platform team
• Not a role free of routine. Recurring work is part of the job, and so is reducing it

• You write expert SQL and have strong dimensional or semantic data modelling skills
• You write working Python, with tests and version control
• You've built metrics and semantic layers
• You have ETL and data warehouse or lakehouse experience, including onboarding sources end to end
• You've used LLMs hands-on in production data work, for example classification, extraction, translation, or agents that other people use
• You work like a consultant to the business: you understand how a process works, propose solutions and frame them in terms of business value
• Databricks and Unity Catalog
• Hands-on experience with, or a solid understanding of, MCP, tool calling, tracing and evals for LLM applications and agents
• Workflow automation tools such as n8n
• CI/CD, orchestration and infrastructure-as-code
• Background in fintech or banking (transaction monitoring, treasury, reconciliation, regulatory data) or another business with a high-volume operational flow

You'll have high autonomy. You choose the tools and the approach, including trialling new vendors and open-source solutions. Sign-off is needed in two cases. The first is architecture: whether a solution scales, holds across use cases and growing volumes, and runs without constant support. The second is anything that feeds high-stakes decisions. Priorities are set by final business impact.

We're a small central team that works with every function, and Finance is our primary partner. We use a modern stack, you're free to try new tools, and there are incentives for driving change. AI assistants take on routine work so people can focus on harder problems. We work in English, think long term, and build data that functions keep developing after we deliver it.