Job Description
📋 Description Own the data layer architecture across relational, columnar, and vector stores. Build scalable ingestion pipelines and star schemas for multi-system data. Design embedding pipelines and RAG context engine for AI agents. Transform data with deduplication and entity resolution for accuracy. Embed DataOps with automated tests, CI/CD, and observability. Implement data governance and AI security measures. 🎯 Requirements 5+ years data engineering; end-to-end data architecture experience. Snowflake and Databricks data warehouses. dbt, Airflow, Airbyte, Fivetran for ETL/ELT. Vector stores and embedding pipelines (pgvector, Pinecone, Weaviate). Dimensional modeling; Iceberg and relational/NoSQL storage. Python, Git, and CI/CD (Jenkins, GitHub Actions). AI security and data governance awareness. 🎁 Benefits Fixed hybrid work model: 3 days in office (Tue-Thu). Modern downtown office with gym and collaborative spaces. Annual All Hands in Vancouver HQ. Competitive salary and comprehensive benefits. Stock options and/or bonus eligibility. Generous paid time off and volunteering days.
