Job description

Please, no third parties. Permanent residents only. Main Duties & Responsibilities: - Take direct, hands-on ownership of building, optimizing, and maintaining the Snowflake data platform including writing production-grade code, executing platform configurations, and resolving issues at the engineering level without reliance on management or coordination layers. - Design and implement warehouse schemas within Snowflake, including dimensional modeling structures such as fact tables, dimension tables, and slowly-changing dimensions that support reliable, performant analytical workloads. - Develop, deploy, and maintain data transformation logic using dbt or comparable transformation frameworks, ensuring data flows are well-documented, testable, and built for long-term maintainability. - Build and support end-to-end data pipelines using orchestration and ingestion tooling including Airflow, Dagster, Fivetran, or Stitch, to move data reliably from source systems into the Snowflake environment. - Write advanced SQL to support pipeline development, query tuning, and schema design; apply window functions, query optimization techniques, and Snowflake-specific features to improve platform performance and cost efficiency. - Develop Python scripts and automation utilities that support pipeline operations, data validation, troubleshooting workflows, and platform maintenance tasks. - Identify, diagnose, and resolve production data issues independently — including broken pipelines, data quality failures, and performance degradation — with a bias toward fast, durable fixes. - Leverage Snowflake Cortex capabilities where applicable, including ML-integrated warehouse features, vector search, and prompt engineering within the Snowflake environment, to extend platform functionality. - Incorporate AI-assisted development tools such as GitHub Copilot or similar platforms to accelerate delivery, improve code quality, and reduce time spent on repetitive engineering tasks. - Contribute to platform documentation, data lineage tracking, and internal knowledge sharing to strengthen the broader engineering team's technical capability over time. Skills & Requirements: - 4+ years of active data engineering or analytics engineering experience, with a clear record of hands-on pipeline and warehouse work. - Hands-on, recent experience with the Snowflake data platform, including direct work with SQL execution, schema design, query optimization, and data loading within Snowflake. - Strong SQL proficiency, including complex joins, subqueries, window functions, and performance-focused query design within a warehouse environment. - Solid Python skills applied to data engineering tasks — scripting, automation, pipeline logic, and operational troubleshooting. - Practical experience with data pipeline and orchestration tools such as Airflow, Dagster, Fivetran, or Stitch, with a clear understanding of how these tools connect source systems to the warehouse layer. - Experience with dbt or a comparable transformation framework for building and managing warehouse transformation logic. - Demonstrated ability to design and implement warehouse architecture from the ground up, including experience with a major platform build, migration, or greenfield implementation. - Comfort using AI-assisted development tools (e.g., GitHub Copilot, ChatGPT) to accelerate coding, debugging, and problem-solving in day-to-day engineering work. - Experience leveraging Snowflake Cortex is preferred. - Snowflake and/or other data engineering certifications are a plus. tags: database engineer, data engineer, snowflake engineer, etl developer, backend engineer, bi developer, bi engineer, business intelligence developer, business intelligence engineer

Requirements

Must-haves

Please, no third parties. Permanent residents only.
Develop, deploy, and maintain data transformation logic using dbt or comparable transformation frameworks, ensuring data flows are well-documented, testable, and built for long-term maintainability.
Build and support end-to-end data pipelines using orchestration and ingestion tooling including Airflow, Dagster, Fivetran, or Stitch, to move data reliably from source systems into the Snowflake environment.
Develop Python scripts and automation utilities that support pipeline operations, data validation, troubleshooting workflows, and platform maintenance tasks.
Identify, diagnose, and resolve production data issues independently — including broken pipelines, data quality failures, and performance degradation — with a bias toward fast, durable fixes.
Leverage Snowflake Cortex capabilities where applicable, including ML-integrated warehouse features, vector search, and prompt engineering within the Snowflake environment, to extend platform functionality.
Incorporate AI-assisted development tools such as GitHub Copilot or similar platforms to accelerate delivery, improve code quality, and reduce time spent on repetitive engineering tasks.
Contribute to platform documentation, data lineage tracking, and internal knowledge sharing to strengthen the broader engineering team's technical capability over time.
4+ years of active data engineering or analytics engineering experience, with a clear record of hands-on pipeline and warehouse work.
Hands-on, recent experience with the Snowflake data platform, including direct work with SQL execution, schema design, query optimization, and data loading within Snowflake.
Strong SQL proficiency, including complex joins, subqueries, window functions, and performance-focused query design within a warehouse environment.
Solid Python skills applied to data engineering tasks — scripting, automation, pipeline logic, and operational troubleshooting.
Practical experience with data pipeline and orchestration tools such as Airflow, Dagster, Fivetran, or Stitch, with a clear understanding of how these tools connect source systems to the warehouse layer.
Experience with dbt or a comparable transformation framework for building and managing warehouse transformation logic.
Demonstrated ability to design and implement warehouse architecture from the ground up, including experience with a major platform build, migration, or greenfield implementation.
Comfort using AI-assisted development tools (e.g., GitHub Copilot, ChatGPT) to accelerate coding, debugging, and problem-solving in day-to-day engineering work.
tags: database engineer, data engineer, snowflake engineer, etl developer, backend engineer, bi developer, bi engineer, business intelligence developer, business intelligence engineer

Nice-to-haves

Experience leveraging Snowflake Cortex is preferred.
Snowflake and/or other data engineering certifications are a plus.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

sqlpythonmlai

Platform: bullhorn
Effort: medium