Senior Data Engineer

Salary Range: 130000 to 160000 (Currency: CAD) (Pay period: per-year-salary)

What You'll Do:

• Architect for the Future: Optimize our existing Snowflake architecture, establishing strict environmental isolation and scalable structures that prepare our data for eventual downstream commercialization and product offerings.

• Drive Agentic Engineering: Leverage tools like Snowflake Cortex, Cursor, and UiPath to automate workflows, build semantic models, and deploy agents that accelerate time-to-value.

• Establish Data Observability: Implement and manage robust data quality and observability frameworks to ensure pipeline reliability and proactive issue resolution.

• Operationalize Machine Learning:Design and maintain MLOps pipelines to support the seamless rollout, monitoring, and lifecycle management of ML models directly within Snowflake.

• Execute Shared Ownership: Partner closely with your peers under the Data Engineering Manager to share responsibilities across pipeline management, MLOps, and architecture, avoiding siloed knowledge and ensuring comprehensive team coverage.

• Model for Enterprise Utility: Synthesize disparate operational entities into a unified, enterprise-wide semantic model that supports both internal analytics and future data monetization efforts.

Qualifications

• 5+ years of Data Engineering experience with a deep, specialized focus on Snowflake's advanced features (e.g., RBAC, materialized views, dynamic tables, Snowpipe, stored procedures).

• Advanced proficiency in SQL and Python, with a strong foundation in applying software engineering best practices to ELT processes.

• Observability Expertise: Hands-on experience implementing data observability and monitoring platforms (such as DataDog) to manage data quality at scale.

• AI & MLOps Exposure: Demonstrated experience using AI-assisted development tools (e.g., Cursor, Cortex) and familiarity with MLOps principles for productionalizing machine learning models.

• Pipeline Management: Experience building and maintaining resilient, low-touch data pipelines using modern integration and orchestration tools (e.g., Fivetran, AWS Glue, AWS Lambda).

What You'll Bring To The Team:

Technical Competency: Advanced SQL skills, proficiency with Python/R, and experience with BI tools. Focus on self-sufficiency and leveraging AI tools to accelerate development.
"Builder" Mentality: An ability to thrive in fast-paced environments with a track record of defining and executing high-impact initiatives. A desire to solve complex problems, remediate technical debt, and find creative solutions for scaling our platform.
Business Acumen: Strong business acumen with a proven ability to translate complex data analysis into strategic recommendations. Adept at identifying key drivers and influencing decision-making. You understand the business behind the data and the path to commercialization.
Empathetic Collaboration: Assertive with humility – able to communicate both persuasively and positively. Maintain high standards for verbal and written communication while seamlessly sharing domain responsibilities across the engineering pod.
Trusted Advisor: Possesses a high degree of integrity, the relentless pursuit of truth, and an ability to inspire change, particularly in championing data quality and observability standards.

What Will Make You Stand Out:

• Deep domain expertise navigating complex merchant payment ecosystems (e.g., Adyen), operating under rigorous enterprise data governance and security standards.

• Proven ability to architect the translation of high-velocity transactional events into highly optimized, columnar analytical architectures.

• Direct experience architecting data products for commercialization, external endpoints, or embedded analytics within a SaaS platform.