Working at the intersection of data engineering and business analytics, you’ll build trusted data models, metrics and entities that power dashboards, reporting and business decisions across Liberis. You’ll take ownership of analytical problems from initial discovery through to production, working closely with data engineers, analysts and business stakeholders.

You’ll also use AI tools such as Claude Code and Codex to accelerate delivery, automate routine work and improve how analytical solutions are developed—while maintaining appropriate review, testing and human oversight.

Success in this role means creating high-quality data products that stakeholders trust, enabling greater self-service and making our analytics platform easier to maintain and evolve.

What you'll get to do:

• Design & build dbt models with a clear layering and data modelling approach.
• Own data model quality through comprehensive testing.
• Build semantic layers (LookML, dbt Semantic Model, or equivalent) to enable self-serve analytics.
• Collaborate across functions to understand business requirements and translate them into data models; provide feedback on downstream usage.
• Use AI tools effectively to accelerate SQL/dbt writing, generate tests, automate routine work, and build an end-to-end harness from plan to production with a proper human-in-the-loop workflow.
• Review peers' work, focusing on design patterns, testing strategy, and architectural decisions.
• Maintain and evolve models based on user feedback; handle schema changes with minimal downstream disruption.
• Automate analytical work for stakeholders using lightweight Python/SQL workflows when appropriate.

The interview process:

• Screening call with Chess - Internal Recruiter (30 mins)
• Video interview with the Hiring Manager (1 hour)
• Technical interview with a member of Engineering team (1 hour)
• Video interview with the wider Liberis team (1 hour)

What we think you'll need:

• Proven hands on analytics engineering, data engineering, or BI engineering experience, working with dbt in production.
• Strong SQL - window functions, CTEs, and complex joins; can explain query performance.
• Dimensional modeling or data modeling experience; understand grain, facts, dimensions, historization, and various data modelling topologies.
• Production judgment - have shipped models used by real stakeholders; can discuss what worked and what didn't.
• Git & CI/CD - comfortable with PRs, code review, and version control.
• AI coding tools - hands-on experience with Claude Code, Cursor, or a similar LLM IDE.

Next steps

If this opportunity feels like the right fit for your next career move, we’d love to hear from you!

Even if you don’t meet every requirement, don’t hesitate to apply or reach out to Chess (Internal Recruiter) on chess.crossley@liberis.com

Our hybrid approach

Working together in person helps us move faster, collaborate better, and build a great Liberis culture. This role requires at least three days per week in the office, with flexibility around which days depending on team and business needs. Our ways of working may evolve over time, including office attendance expectations. At Liberis, we embrace flexibility as a core part of our culture, while also valuing the importance of the time our teams spend together in the office.