This is a Hybrid role, based in Vancouver, Canada

About the Role

Freight Club is building out its Analytics & Insights function, and we're looking for an Analytics Engineer to help build the data foundation that everything else depends on. This is a hands-on, build-focused role for someone who enjoys working in the layer between raw data and analytics modelling data and building the pipelines that turn messy source data into clean, trusted, well-structured datasets.

You'll work closely with the Director of Analytics and Insights and alongside the wider analytics team. You won't do this alone - you'll be part of a small team, with guidance on architecture and direction - and you'll play a key role in how our data is transformed and made ready for analysis, reporting, and in time machine learning. It's a great fit for someone with a few years of experience who is ready to grow their scope.

The immediate focus is helping to audit and rebuild our data foundation on Databricks using a medallion architecture, so the business can be served with clean, reliable data it can trust - using solid SQL and Python skills, and modern AI tooling, along the way.

Why This Role Exists

This person helps build the reliable, well-modelled data layer that makes trustworthy reporting and advanced analytics possible for the business.

What You'll Do

These are shared team priorities - you'll focus on the hands-on engineering work and partner with the analytics team and the Director of Analytics and Insights on architecture decisions and the rest.

Build the Foundation

• Help audit the current warehouse. Review existing tables and data quality, and contribute to the target-state design and migration plan.
• Build out the lakehouse. Help implement a medallion architecture on Databricks - structuring raw, cleansed, and business-ready layers (bronze / silver / gold).
• Model the data. Build well-documented data models (e.g. dimensional / star-schema) that are intuitive for analysts and reliable for reporting.
• Build the pipelines. Develop and maintain ELT/ETL pipelines using SQL and Python/PySpark, with Delta Lake as the storage foundation.

Make the Data Trustworthy & Usable

• Improve data integrity. Build testing, validation, and data-quality checks so the business can trust the numbers.
• Automate reporting foundations. Replace manual refreshes with reliable, scheduled, automated data pipelines feeding Power BI and other consumers.
• Enable clean BI. Help build the well-structured model layer that Power BI reports sit on, and support the clean-up of existing reporting.
• Document and follow good practices. Write clear documentation and follow team conventions and engineering practices (version control, code review, CI/CD for data) so the platform is maintainable.

Support Advanced Analytics, ML & AI

• Prepare data for ML. Build clean, reliable datasets and feature-ready tables that make advanced analytics and machine-learning models possible as the team grows its capabilities.
• Use modern AI tools. Use current AI tooling - including LLM-based assistants such as Claude - to speed up development (SQL, Python, pipelines, documentation).
• Partner with the team and the business. Work with the analytics team and business stakeholders to understand how data is consumed, and design models that fit real analytical and business needs.

Who You'll Work With

You'll work most closely with the Director of Analytics and Insights and day to day with the wider analytics team. The business is the primary consumer of the data models, pipelines, and reporting you help build, so you'll collaborate with stakeholders across the company to understand the data they rely on.

What We're Looking For

Our ideal candidate is a hands-on analytics or data engineer with strong experience who takes pride in well-modelled data and is excited to help bring order to a messy environment. The traits below matter as much as the technical checklist that follows.

• A builder at heart. You enjoy building data models and pipelines, and you care about doing it well, not just quickly.
• Curious about architecture. You think in layers, models, and data flows, and want to grow your data architecture skills.
• A self-starter. You're comfortable in a less structured environment, ask good questions, and take initiative on your work.
• Quality-focused. You treat data integrity, testing, and documentation as part of the job, not an afterthought.
• A collaborative translator. You understand how analysts and the business use data, and design your models and pipelines around those needs.
• AI-curious. You actively use modern AI tooling, such as Claude, to work faster, and you're interested in how ML models use data.

Skills & Experience

Area

What we expect

Databricks & Spark

Hands-on experience with a modern data platform - Databricks preferred (Spark / PySpark, Delta Lake), or similar such as Snowflake.

Lakehouse concepts

Understanding of layered data design (e.g. bronze / silver / gold); hands-on medallion experience is a plus.

Data warehousing

Good grasp of data warehouse/lakehouse fundamentals, with interest in contributing to design and migration work.

Data modelling

Working data modelling skills (dimensional / star-schema and similar) for analytics-ready datasets.

SQL

Strong SQL for transformation and validation - a core, everyday tool in this role.

Python & data manipulation

Solid Python (PySpark a plus) for data manipulation, transformation, and automation.

Machine learning

Basic understanding of ML concepts and what data models need.

AI stacks & LLMs

Comfort using modern AI tooling, including LLM assistants such as Claude, to speed up development.

Pipelines & orchestration

Experience building and scheduling ELT/ETL pipelines (e.g. Databricks Workflows, Airflow, or similar).

Data quality & integrity

Testing, validation, and data-quality practices that make data trustworthy.

BI enablement

Familiarity with Power BI and building the clean model/semantic layer that reporting depends on.

Engineering practices

Version control (Git), code review, and CI/CD applied to data / analytics engineering.

Nice to Have

• dbt or similar transformation frameworks.
• Experience on a major cloud platform (Azure, AWS, or GCP).
• Exposure to ML workflows or feature engineering.
• Experience working in an agile, sprint-based environment.

Why It's a Great Opportunity

This is a great chance to help build a data platform from the foundations up - working on the lakehouse the whole business will rely on, with mentorship from analytics leadership. If you want to grow your skills and help take an organisation from manual, ad-hoc data to a clean, automated, ML-ready platform, this is the role.

Compensation and Benefits

Salary - $100k-$120k per annum

• Health and wellness support for you and your family, including an employee assistance program
• 100% paid premiums for health and dental benefits in Canada
• Paid time off
• Easy access to online and phone-based counselling services
• A genuinely hybrid-flexible work environment

Join Our AI Journey at Cymax Group!

At Cymax Group, we thrive on innovation and entrepreneurship. As an AI-first organization, we are on an exciting journey to continuously grow and evolve our focus on AI, empowering our people at all levels. Our fast-paced environment encourages agility and nimbleness, enabling our team to adapt swiftly to changing market dynamics. We're passionate about continuous learning and growth, offering opportunities for professional development. Our data-driven decision-making ensures we make informed choices that lead to success. If you're excited about making an impact and being part of a dynamic, AI-empowered team, you'll feel right at home here.

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