Are you driven by data, customer success, and bold innovation? If you're ready to help shape the future of ecommerce technology, Cymax Group Technologies wants you on our team.

At Cymax Group

Technologies, we empower leading brands and retailers to thrive in the digital marketplace through cutting-edge technology, data-driven insights, and seamless logistics. We’re a tech-driven brand accelerator in home, lifestyle, and adjacent categories. Our proprietary platforms—Channel Gate and Freight Club—leverage automation, AI, and over two decades of ecommerce expertise to simplify multi-channel selling, optimize fulfillment, and unlock scalable growth.

We support thousands of partners across North America, helping them navigate complex marketplaces like Amazon, Walmart, Wayfair, and more. Our mission is to make ecommerce effortless, and our team is at the heart of that transformation. Recognized as one of Canada’s fastest-growing tech companies, Cymax Group is proud to foster a culture where innovation, collaboration, and employee experience come first.

If you're passionate about making an impact and being part of a company that’s redefining online retail, we’d love to meet you.

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 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.

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. This person helps build the reliable, well-modelled data layer that makes trustworthy reporting and advanced analytics possible for the business. 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. 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. Write transparent 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). Work with the analytics team and business stakeholders to understand how data is consumed, and design models that fit real analytical and business needs. 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. 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. You enjoy building data models and pipelines, and you care about doing it well, not just quickly. You think in layers, models, and data flows, and want to grow your data architecture skills. Quality-focused. You treat data integrity, testing, and documentation as part of the job, not an afterthought. 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. Databricks &

• Spark Hands‑on experience with a modern data platform — Databricks preferred (Spark / PySpark, Delta Lake), or similar such as Snowflake. Understanding of layered data design (e.g. bronze / silver / gold)

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

Health and wellness support for you and your family, including an employee assistance program ~100% paid premiums for health and dental advantages in Canada ~ Easy access to online and phone-based counselling services ~ A genuinely hybrid-flexible work setting Join Our AI Journey at Cymax Group! 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. 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. Include shift schedule

📌 Performance Management Engineer (Vancouver)
🏢 Cymax Group Technologies
📍 Vancouver