Senior Business Intelligence Engineer

Braze Braze · Enterprise · Toronto, ON · Growth

Senior Business Intelligence Engineer role focused on building and maintaining data pipelines, data models, and metrics using dbt and Snowflake to support decision-making. The role involves partnering with stakeholders, defining metrics, ensuring data quality, and mentoring other engineers.

What you'd actually do

  1. Partner with stakeholders to clarify requirements and translate them into scalable data models, definitions of metrics, and sustainable dbt implementations
  2. Own and evolve the semantic layer alongside curated warehouse models: define reusable metrics and dimensions
  3. Partner with analytics and data engineering on the metric lifecycle (definition, approval, deprecation), documentation, and lineage from semantic objects through to downstream consumers
  4. Raise the bar on data quality: design and maintain tests, monitoring to keep metrics consistent as the business scales
  5. Design and communicate data flows, dependencies, and SLAs for your areas; coordinate with Data Engineering and platform teams where orchestration, warehouse behavior, or access patterns affect reliability or cost
  6. Mentor engineers and analysts through code review, pairing, and shared standards so the whole BI engineering practice ships faster with fewer incidents

Skills

Required

  • Business intelligence
  • Analytics engineering
  • Data engineering
  • Cloud data warehouses
  • SQL
  • Data modeling
  • dbt
  • Snowflake
  • Semantic layer modeling
  • Metric definition
  • Dimension modeling
  • Orchestrated pipelines (e.g., Airflow)
  • Communication skills
  • Stakeholder management

Nice to have

  • Mentoring engineers and analysts

What the JD emphasized

  • 5+ years of experience in business intelligence, analytics engineering, or data engineering
  • Expert-level SQL and strong data modeling skills and metric design that survives real-world edge cases
  • Deep hands-on experience with dbt (projects, packages, tests, documentation, CI-oriented workflows) and Snowflake (or a comparable warehouse), including optimization and operational awareness
  • Hands-on experience building or maintaining a semantic layer—modeling metrics and dimensions for governed consumption, with clear documentation, drill and aggregation behavior, and access patterns suited to both self-serve and curated reporting