HQ - Senior Analytics Engineer

The role

We're a small, well-established Analytics Engineering team sitting inside the HQ Data Team and serving the rest of the business — Finance; Product Strategy; Go-to-Market; Sales, Branding, and Marketing; and Software Engineering. We've recently completed our migration from Redshift to BigQuery, and we're adopting dbt Fusion. The development loop runs through the official dbt VS Code extension and dbt-lsp. Looker is our BI layer; dbt Elementary backs our observability.

Alongside the day-to-day modelling work, we're assembling an agentic layer over the codebases and collaboration tools we interact with — dbt, BigQuery, Airflow, Terraform, GitHub, Jira, Notion, and Slack. The team uses Claude Code, and we are building an internal plugin marketplace of skills, commands, and agents for our work. We expect this kind of tooling-driven leverage to become a defining part of Analytics Engineering, and we'd rather be in front of that shift than carried along by it. We want someone who's excited to be in the middle of it — not just a consumer of AI tools, but someone who can help shape what the team builds.

This is a role for someone who treats Analytics Engineering as a craft. We care about modelling clarity, performance and accuracy, documentation that's actually useful, and codebases that age well. We care about the meaning and value of the data we handle, not just the pipes that move it — about governance and semantics as first-order concerns. We're close followers of where Analytics Engineering is going as a discipline. We're also honest about the realities: Job&Talent moves fast, our recruitment and workforce management platform is constantly evolving, and our data sources are moving targets. dbt projects grow organically in that environment, and ours shows it — improving it is part of the job.

Responsabilities

• Modelling raw data into clean, well-tested, well-documented dbt models that our stakeholder teams can rely on
• Owning end-to-end delivery on stakeholder requests — from understanding what they actually need, through modelling and testing, to LookML and dashboards
• Contributing to the team's modelling standards, dbt conventions, and the engineering practices around our development and deployment workflow
• Building and refining agentic tooling — skills, commands, and agents in our plugin marketplace — that raises the floor for everyone on the team
• Working closely with the Data Engineering team on source-side issues, schema changes, and ingestion patterns.

Requirements

• 5+ years of professional experience in data transformation and modelling, including substantial commercial dbt experience (we'd expect 2+ years of serious dbt work, not "I've used it")
• Genuine SQL depth — you can read a complex model and reason about its performance, correctness, and maintainability without running it
• Strong grounding in dimensional modelling (Kimball or related traditions); fluency with at least one cloud warehouse, ideally BigQuery
• Experience building and maintaining the BI layer for stakeholders — we use Looker/LookML, but equivalent semantic-layer experience transfers
• Clear written and verbal communication. You'll be talking directly to people across Finance, Product Strategy, and Software Engineering, often translating between them
• Comfort and curiosity with AI-augmented development — Claude Code, Copilot, or similar. You don't need to be an expert, but you should be actively using these tools and forming opinions about them
• Hands-on experience driving the full business stakeholder loop — intake, scoping, iteration, and sign-off — understanding what they actually need, not just building what you're handed.
• Fluent English

Desirable

• Experience with dbt's newer tooling and surrounding ecosystem — dbt Fusion, VS Code extensions, dbt packages etc.
• Python at the level needed to read and modify orchestration code and write small utilities
• Refactoring or warehouse-migration experience…