Design, build, and maintain ETL/ELT pipelines using Databricks (PySpark, Delta Lake).
Optimize pipelines for performance, cost efficiency, and scalability within GCP.
Develop batch and streaming data processes using Spark Streaming and related technologies.
Implement data solutions leveraging GCP services such as BigQuery, Cloud Storage, Dataflow, Cloud Composer, and Vertex AI integrations.
Apply best practices for cloud security, IAM configuration, monitoring, and cost management.
Build and maintain data models, including dimensional modeling and data vault structures.
Implement data quality frameworks, validation rules, and automated testing.
Managing data versioning, governance, and lineage using tools such as Unity Catalog or GCP Data Catalog.
Partner with cross-functional teams to gather requirements and translate them into technical designs.
Provide technical guidance and influence engineering best practices across the team.
Contribute to documentation, architectural diagrams, and knowledge sharing.

Data Engineer or similar role.
Strong hands-on experience with Databricks, including: PySpark/Spark, Delta Lake, Databricks workflows/jobs
Proficiency with GCP: BigQuery, Cloud Storage, Dataflow or Dataproc
Strong coding skills in Python and SQL
Solid understanding of distributed systems, data warehousing, and data architecture principles.
Experience with CI/CD tools (GitHub, GitLab, Azure DevOps, or similar).

Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade
Company paid holidays
Personal Days
Sick Leave
Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
Life and disability insurance
Employee assistance programs
Other benefits as provided by local policy and eligibility

Data Engineer, Databricks, Google Cloud Platform (GCP)

Key skills