Analytics Data Engineer

About the job
Analytics Data Engineer

** Must be willing to work on-site in Markham office 5 days/week **

We’re seeking an Analytics Data Engineer to help shape the future of dental supply distribution through data-driven innovation.

Key Responsibilities:

We’re looking for a hands-on Analytics Data Engineer who will own our end-to-end data stack – building and tuning data warehouse, automating reliable SQL/Python ELT pipelines, and delivering analytics and semantic models for dashboards and AI/ML use cases, while maintaining strong data-quality and governance standards

Data Engineering

Hands-on data strategy lead – Translate business goals into an actionable roadmap for modern data warehousing, governed data pipelines, and scalable infrastructure.
Data-warehouse – Architect, build, and continuously tune our data lakehouse architecture, ensuring high availability, cost-efficiency, and strong performance across all teams.
Pipeline builder – Design, automate, and monitor ELT workflows using SQL and Python, with comprehensive testing and alerting to guarantee accuracy and availability
Governance & quality – Implement version control, data lineage tracking, and access policies; enforce data SLAs and lead root-cause analysis to uphold data integrity and compliance.
Integration specialist – Connect and integrate data from various enterprise systems (including CRM and ERP platforms such as HubSpot, Acumatica, RingCentral) into the data platform, ensuring a unified, analytics-ready data model.

Analytics

Analytics enabler – Build and maintain Power BI semantic models, dashboards, and self-serve tools that turn raw data into actionable insights for business stakeholders.
AI/ML facilitator – Collaborate with the data Team to prepare clean, usable feature sets and deploy predictive models that drive revenue, operational efficiency, and customer value.
Cross-functional collaborator – Collaborate with stakeholders to resolve data issues, and promote agile, insight-driven decision-making.

Qualifications & Experience:

5+ years experience in data engineering, analytics, or a related field.
End-to-end ownership: Demonstrated success owning end-to-end data pipelines and business intelligence assets in a high-growth, fast-moving organization (e.g., building ETL workflows and delivering BI dashboards).
Data & AI projects: Experience partnering with data science teams to deploy predictive or generative AI models (leveraging Python-based ML frameworks or cloud AI services) that lifted revenue and reduced operational costs.
Modern data stack: Deep understanding of modern data stack technologies and cloud platforms (AWS or Azure), including hands-on experience with AWS managed services such as S3, AWS Lake Formation, Glue, EMR, EC2, AWS Lambda, Athena, and comparable Azure data services.
Security & governance: Strong background in data security and governance – implementing data encryption, access controls/roles, and data lineage tracking (familiarity with tools like AWS Lake Formation or Azure Purview for data cataloging is a plus).
SQL & Python expertise: Expert at writing analytical SQL (in databases or warehouses like PostgreSQL, MySQL, Snowflake, or Amazon Redshift) and building ELT jobs in Python (using frameworks like dbt, Apache Airflow, or AWS Glue). Able to create complex DAX measures in Power BI and design robust data pipelines that scale.
Strategic execution: Demonstrated ability to turn strategic business objectives into a concrete plan for data warehousing, governed analytics, and practical AI/ML deployment (for example, aligning cloud data platforms and machine learning services like AWS SageMaker or Azure ML with business goals).
Cloud data warehousing: Skilled at architecting, implementing, and continuously optimizing scalable cloud data warehouses (for example, solutions on Snowflake, Amazon Redshift, or Databricks Lakehouse) to keep high-quality, analytics-ready data flowing to the business.
System integration: Experience integrating data from CRM and ERP systems (e.g. HubSpot CRM, Acumatica ERP) via APIs or connectors, merging these data sources into a unified analytics platform.

*Relevant industry certifications are a plus – for example, AWS or Azure cloud certifications, or data platform certifications like Snowflake or Databricks.

Why join us? (Brace yourself…)

We offer competitive compensation
Extended Health and Dental Benefits Plan (100% Employer-paid Premiums on eligible benefits)
Continuous Learning Assistance Program reimbursing up to $1,500 per calendar year
Corporate Goodlife Membership Discount
Group RRSP (Non-match)
15 Vacation Days to start and scale to 20 Vacation Days.
Paid Religious Holidays
Paid Volunteer Days
Flexibility to help you balance work and life
Supportive leadership team and Culture Committee that works to maintain a positive culture
Competitive yet collaborative work environment
Fully stocked snacks, team BBQs and other company paid meals (we love to eat!)