Business Operations Co-op

Clio•Vancouver, BC

3d•CA$46,000•Hybrid

About The Position

Clio is looking for a Data Modeling Co-op to join our Data Insights team for an 8-month term. You will work alongside senior analytics engineers to build and maintain the data models that power Clio’s business intelligence, reporting, and data products. This is a hands-on role — you will write real dbt models, contribute to our production data warehouse, and collaborate directly with business stakeholders. This role is ideal for a student who is curious about how data powers business decisions and wants to go beyond dashboards to understand the engineering underneath.

Requirements

Enrolled in a degree program in Computer Science, Data Science, Statistics, Engineering, or a related field.
Solid SQL skills — comfortable writing queries with JOINs, aggregations, and CTEs.
Familiarity with at least one programming or scripting language (Python preferred).
Ability to work independently, ask good questions, and communicate clearly with teammates.
Genuine interest in how data is structured and how it powers business decisions.

Nice To Haves

Exposure to dbt (Core or Cloud) — even from a course, tutorial, or personal project.
Experience with a cloud data warehouse (Databricks, Snowflake, BigQuery, or Redshift).
Familiarity with BI tools such as Looker, Tableau, or Power BI.
Understanding of data modeling concepts (star schemas, fact/dimension tables).
Experience with Git and version control workflows.

Responsibilities

Build and maintain dbt models that transform raw source data into clean, tested, and well-documented datasets for downstream use by analysts, BI tools, and business stakeholders.
Write and optimize SQL queries to answer business questions and support ad hoc data requests.
Contribute to data quality by writing dbt tests and helping monitor data freshness and accuracy.
Partner with team members across Data Engineering, BI, and business units to understand requirements and translate them into data models.
Help document data assets — model descriptions, column-level metadata, and business definitions — to support self-service analytics.
Participate in code reviews and apply software engineering best practices (Git, pull requests, CI/CD) to data work.
Support ongoing projects across areas like revenue modeling, product usage analytics, or go-to-market data pipelines, depending on team priorities during your term.