Data Platform Engineer

Role overview

Build and operate the data infrastructure behind analytics, AI workflows, and operational reporting. The role combines ingestion and transformation engineering with data quality, governance, and reliability, helping make organizational data dependable and usable by downstream teams.

Responsibilities - Develop ingestion pipelines from databases, APIs, event streams, and file systems. - Create documented transformation layers using SQL, Python, dbt, or similar tools. - Maintain cloud warehouse or lake architectures for analytical and operational data. - Add data quality checks, schema validation, and contracts at pipeline boundaries. - Improve cataloging, metadata management, and lineage documentation. - Coordinate with analytics and ML teams on downstream data product needs. - Monitor pipeline reliability, freshness, and performance, and resolve issues systematically.

Requirements - Production experience building and operating data pipelines and transformation layers. - Strong SQL skills and experience with an orchestration framework such as Airflow, dbt, Prefect, or Dagster. - Deep experience with at least one cloud data warehouse or lake platform, such as Snowflake, BigQuery, Databricks, or Azure Synapse. - Understanding of data quality, schema management, and pipeline operations. - Ability to document data assets and work across analytics and engineering teams.

Benefits and work setup - Full-time role, based in Toronto or Montréal, or remote within Canada.