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Overview

This opportunity is for an Analytics Engineer role in Canada, presented on behalf of a partner organization that oversees the hiring process and all next steps. The position blends data engineering, analytics engineering, and client consulting. You will help create modern data environments for multiple clients, turning raw information into dependable, decision-ready insights.

The work is highly collaborative and hands-on, with an emphasis on building scalable, end-to-end data solutions. Much of the work will involve greenfield initiatives, where you will help establish the underlying data architecture from the ground up. The role is suited to someone who enjoys solving complex data challenges, working directly with clients and stakeholders, and delivering visible business impact through well-structured, documented, and reliable data systems.

Key Responsibilities

Design, develop, and support scalable data pipelines that ingest, transform, and deliver trusted datasets.
Use SQL and Python to build analytics engineering workflows and data processing logic.
Work with business partners to convert requirements into practical data models and technical solutions.
Create and maintain data models that follow architecture standards and engineering best practices.
Leverage modern data platforms such as Snowflake, Databricks, or Microsoft Fabric for analytics workloads.
Set up CI/CD workflows for data assets to improve reliability, repeatability, and scale.
Strengthen data quality through testing, monitoring, observability, and clear documentation.
Enable BI tools and downstream users with dependable, well-designed access layers.
Investigate and fix production performance or system issues when they arise.
Continuously look for better tools, methods, and practices to improve delivery.

Requirements

At least 3 years of experience in data engineering, analytics engineering, or a similar data-centric role.
Advanced SQL skills for complex queries, transformation logic, and optimization.
Strong working knowledge of Python for automation and data handling.
Practical experience with cloud data platforms such as Snowflake, Databricks, or Microsoft Fabric.
Experience creating or supporting CI/CD pipelines in a data environment.
Understanding of BI tools and how data supports reporting and analytics use cases.
Good grasp of relational databases, data modeling, and data warehouse concepts.
Experience with data quality practices, including testing, monitoring, and observability.
Ability to collaborate with business teams such as finance and operations to deliver solutions.
Comfort working in fast-moving consulting settings with strong adaptability and problem-solving ability.
Excellent communication skills, including the ability to explain technical ideas to non-technical audiences.
Must be located in Vancouver or Toronto, Canada.

Benefits

Base pay between CA$75,000 and CA$135,000 annually.
Annual performance bonus of 15% to 25% of base salary.
Remote-first arrangement with flexibility based on project requirements.
Chance to contribute to a variety of client projects across different industries.
Exposure to modern data stacks and advanced analytics technologies.
Room to grow along both technical and consulting career paths.
Supportive, collaborative environment that values learning and innovation.
Occasional travel for client workshops and strategic working sessions.
Opportunity to lead greenfield initiatives with strong ownership and visibility.

Additional Information

This role is managed through a partner company, which handles application review and the next stages of the process. Applications are assessed using an AI-supported matching workflow designed to compare candidates against the role’s core requirements. Shortlisted profiles are then shared with the hiring company, which makes the final interview, assessment, and hiring decisions.

Data Privacy Notice

By applying, you acknowledge that your personal data may be processed to evaluate your fit for the role and shared with the employer for recruitment purposes. This processing may rely on legitimate interest and pre-contractual measures under applicable data protection laws, including GDPR. You may request access, correction, deletion, or objection to processing at any time.

AI tools may also be used to help with parts of the recruitment workflow, such as reviewing applications, analyzing resumes, or identifying inconsistencies in submitted materials. These tools support the recruitment team but do not replace human judgment, and final hiring decisions are always made by people.

Analytics Engineer