Data Modeller

Position: Data Modeller (12 Month Contract)
Clio is the global leader in legal AI technology, empowering legal professionals and law firms of every size to work smarter, faster, and more securely.

We are transforming the legal experience for all by bettering the lives of legal professionals while increasing access to justice .

Summary:

We are currently seeking a Data Modeller to join our Business Operations Team. This role is available to applicants based in Vancouver, Calgary, or Toronto.

Who you are:

We are looking for someone who is passionate about data and enjoys solving business problems through thoughtful, scalable data solutions. You thrive in a collaborative, fast-paced environment and are excited to partner with technical and non-technical stakeholders to make data more accessible, trustworthy, and actionable.

You are curious, detail-oriented, and continuously looking for ways to improve how data is modelled, governed, and consumed. You enjoy learning new technologies—including AI-powered development tools—and applying them to improve productivity and deliver better outcomes.

What your team does:

Our Data Modelling team is part of the broader Data Insights Team. We build trusted, scalable data products that empower teams across Clio to make informed decisions. By transforming complex data into reusable, well-governed models, we enable self-service analytics, operational reporting, product insights, and emerging AI initiatives.

What you'll work on:

Design, build, and maintain scalable data models:
Transform raw and complex data into well-tested, reusable data models using dbt (or similar tools) that support analytics, dashboards, operational reporting, self-service analytics, and AI-ready data products.

Partner with stakeholders and communicate clearly:
Collaborate with Product, Revenue Operations, Finance, Data Engineering, and business stakeholders to translate business questions into trusted datasets, and present technical concepts and findings in a way that is clear and actionable for both technical and non-technical audiences.

Ensure data quality and reliability:
Develop automated tests, monitoring, and validation to ensure data freshness, schema stability, and logic accuracy, and to proactively resolve data quality issues.

Promote data modeling best practices:
Contribute to standards, documentation, and governance that improve consistency, scalability, and long-term maintainability across the analytics ecosystem.

Leverage AI to improve productivity:
Use AI-powered development tools and assistants to accelerate data modeling, improve code quality, and contribute to AI-ready data pipelines and workflows.

What you bring:

2–4 years of experience in analytics engineering or data modeling, ideally in SaaS, building and maintaining production data models from raw source data across multiple systems.

Deep SQL skills and hands-on experience working directly in databases and data warehouses, with Python or a similar language an asset. This role is focused on the backend modeling layer rather than visualization or dashboard building.

Solid understanding of dimensional modeling and data warehousing concepts, with familiarity working in cloud data warehouses (e.g., Databricks, Redshift, or Snowflake).Experience building data models with dbt or a similar transformation framework is an asset, though not required if you bring strong SQL and database fundamentals.

Ability to translate business requirements into reusable data models and collaborate effectively with technical and non-technical stakeholders.

Fluency working with AI-powered development tools—such as VS Code, Claude Code, and similar assistants—and the ability to apply skills, agents, and automation to accelerate data modeling work.

Bonus points if you have:

Experience with Looker or…