Data Modeller (12 Month Contract)

Job summary

Design, build, and maintain scalable data models to support analytics, operational reporting, and AI-ready data products. Collaborate with cross-functional stakeholders to translate business requirements into trusted, reusable datasets while ensuring data quality and governance.

Job details

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 other modern BI platforms. Experience implementing data quality testing and monitoring frameworks. Experience supporting self-service analytics Experience working with AI-assisted development tools such as GitHub Copilot, Cursor, or similar technologies. Exposure to building data products that support machine learning or generative AI applications. This role is a backfill for an existing position. What you will find here: Compensation is one of the main components of Clio’s Total Rewards Program. We have developed a series of programs and processes to ensure we are creating fair and competitive pay practices that form the foundation of our human and high-performing culture. Some highlights of our Total Rewards program include: Competitive, equitable salary with top-tier health benefits, dental, and vision insurance Hybrid work environment, with expectation for local Clions (Vancouver, Calgary, Toronto, Dublin, London, New York City and Sydney) to be in office min. twice per week. Flexible time off policy, with an encouraged 20 days off per year. $2000 annual counseling benefit RRSP matching and RESP contribution Clioversary recognition program with special acknowledgement at 3, 5, 7, and 10 years The expected salary range for this role is $93,700 to $126,700 CAD. Initial placement within the range is informed by geographic region, experience, and skillset, with room to progress as impact and tenure grow. Final offer amounts will vary based on candidate profile. Diversity, Inclusion, Belonging and Equity (DIBE) & Accessibility Our team shows up as their authentic selves, and are united by our mission. We are dedicated to diversity, equity and inclusion. We pride ourselves in building and fostering an environment where our teams feel included, valued, and enabled to do the best work of their careers, wherever they choose to log in from. We believe that different perspectives, skills, backgrounds, and experiences result in higher-performing teams and better innovation. We are committed to equal employment and we encourage candidates from all backgrounds to apply. Clio provides accessibility accommodations during the recruitment process. Should you require any accommodation, please let us know and we will work with you to meet your needs. Learn more about our culture at clio.com/careers We're a Human and High Performing AI company, meaning we use artificial intelligence to improve all of our operations. In recruitment, AI helps us streamline the process for greater efficiency. However, we've built our systems to ensure that a human always reviews AI-generated output, and we never make automated hiring decisions. Disclaimer: We only communicate with candidates through official @clio.com email addresses.

What you’ll do

Design, build, and maintain scalable data models to support analytics, operational reporting, and AI-ready data products. Collaborate with cross-functional stakeholders to translate business requirements into trusted, reusable datasets while ensuring data quality and governance.

Requirements

Requires 2–4 years of experience in analytics engineering or data modeling with deep SQL skills and familiarity with cloud data warehouses. Candidates should have experience with transformation frameworks like dbt and the ability to communicate technical concepts to non-technical stakeholders.

Benefits

• Health insurance • Dental insurance • Vision insurance • Hybrid work environment • Flexible time off • Counseling benefit • RRSP matching • RESP contribution • Clioversary recognition program

Listed skills

• SQL · Preferred
• Stakeholder Management · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Data modeling
• SQL
• Analytics engineering
• Dimensional modeling
• Data warehousing
• dbt
• Python
• Databricks
• Redshift
• Snowflake
• Data quality
• Business intelligence
• AI-powered development tools
• Stakeholder management
• Data governance
• Revenue Operations
• Claude Code
• GitHub Copilot
• VS Code
• Business Problems
• Generative Artificial Intelligence
• Business Operations
• Workflow Management
• Analytical Dashboard
• Curiosity
• Snowflake (Data Warehouse)
• Artificial Intelligence
• Data Analysis
• Automation
• Business Intelligence
• Dashboard
• Business Requirements
• Software As A Service (SaaS)
• Communication
• Data Engineering
• Data Modeling
• Data Quality
• Data Warehousing
• Programming Tools
• Dimensional Modeling
• Finance
• Equities
• Governance
• Scalability
• Innovation
• Python (Programming Language)
• Machine Learning
• Maintainability
• Operational Databases
• Operational Reporting

Job areas

• Data & Analytics
• Technology
• Software
• Engineering
• Data Modeler
• Data Scientist
• Systems Analysts
• Data Scientists