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About the Role

As an Analytics Engineer, you will be responsible for transforming raw retail data into trusted, scalable assets and insights. You will contribute to data models that power business solutions and build data products, semantic layers, and self-service tools to enable independent data access for all teams.

Responsibilities

• Support end-to-end data transformation, converting raw omnichannel source data into clean, structured layers within Snowflake.
• Contribute to the design and build of Snowflake data mart components, applying dimensional modeling best practices.
• Support Power BI report development end-to-end, enabling self-service data access for various teams.
• Perform ad hoc analysis using SQL and Python directly in Snowflake to answer business questions and deliver insights.
• Assist in maintaining data quality standards, including testing frameworks, pipeline alerting, and documentation.
• Support the development and maintenance of Gen AI data solutions, including natural language interfaces.
• Partner cross-functionally to understand analytical needs and translate them into scalable data products.
• Participate in the continuous improvement of the data platform, contributing ideas for new tools, patterns, and AI capabilities.

Requirements

• 3+ years of experience in analytics engineering, data engineering, business intelligence, or an equivalent data analytics role.
• Strong SQL proficiency and hands-on experience with Snowflake or a comparable cloud data warehouse.
• Hands-on experience with ELT/ETL platforms, with Matillion or a similar cloud-native transformation tool preferred.
• Proficiency building data models and dashboards with Power BI.
• Solid understanding of dimensional modeling methodologies including star schemas and slowly changing dimensions.
• Experience with version control using Git.
• Familiarity with data quality practices including testing, documentation, and pipeline monitoring.
• Exposure to Gen AI solutions such as Snowflake Cortex or Microsoft Fabric Copilot is preferred.
• Retail or apparel industry experience across omnichannel environments is an asset.
• Python proficiency for data transformation, ad hoc analysis, or automation is a plus.

Skills

• SQL
• Snowflake
• ELT/ETL
• Matillion
• Power BI
• Dimensional Modeling
• Star Schemas
• Slowly Changing Dimensions
• Git
• Data Quality
• Testing
• Documentation
• Pipeline Monitoring
• Gen AI
• Snowflake Cortex
• Microsoft Fabric Copilot
• Python

Location

• Canada

Work Type

• Full-time

Experience Level

• 3+ years

About the Company

• Established in 1973, Roots is a proudly Canadian lifestyle brand that celebrates authenticity, quality, and community.
• Inspired by the great outdoors, we create timeless, sustainable apparel and leather goods designed for comfort and everyday adventure.
• At Roots, we believe in fostering a culture of inclusivity, creativity, and teamwork – where every team member plays a role in shaping our brand’s legacy.
• If you bring passion to everything you do, lead with integrity, and believe in the power of collaboration to drive growth, we’d love to have you on our journey!

Equal Opportunity

• At Roots we appreciate that skills and expertise are cultivated through a range of experiences.
• We are committed to reflecting Canada's diverse landscape in our products, team, and workplace culture.
• We value your unique perspective and encourage you to apply, even if you don't meet every listed requirement.
• Accommodations are available for applicants throughout the recruitment process.
• Please note: Roots uses technology-assisted tools, including artificial intelligence (AI), to support parts of the recruitment process.
• These tools may be used to help review applications, assess qualifications, and support interview documentation.
• All hiring decisions are reviewed and made by our Talent team and key leaders involved in the recruitment process.
• The posted salary range is intended to reflect the competitive market value of this role and is provided to support transparency in our hiring process.
• Final compensation will be determined based on a variety of factors, including but not limited to internal equity, relevant skills, demonstrated knowledge, experience, and overall fit for the position.