Responsibilities
- Integrate data from BigQuery, Postgres, and Airtable to deliver clean, usable datasets for AI, analytics, and workflow systems
- Develop and manage dbt models that transform marketing data into standardized dimensional schemas using Kimball methodology
- Manage all SQL assets including queries, stored procedures, views, materialized views, and scheduled routines
- Improve data warehouse efficiency and reduce costs through query optimization, partitioning, clustering, and incremental processing
- Orchestrate data workflows using Airflow or comparable tools
- Convert one-off SQL scripts into version-controlled, tested, and documented procedures
- Enhance system performance and reduce expenses via strategic partitioning, clustering, and materialization
- Proactively identify and resolve performance issues before they affect downstream consumers
- Take full ownership of all production-level stored procedures, scripted routines, and scheduled queries in BigQuery and Postgres
- Create new stored procedures for batch data transformations, reporting, and AI/ML feature preparation
- Maintain an up-to-date inventory of stored procedures with clear ownership, dependencies, and operational runbooks
- Design data schemas and build dbt models that standardize input from marketing platforms like Google Ads, Meta, and LinkedIn
- Enforce data quality by implementing dbt tests for uniqueness, non-null values, referential integrity, and custom business rules
- Manage incremental models for large tables, balancing cost efficiency and data freshness
- Own the documentation and lineage tracking within dbt for full transparency
- Schedule, monitor, and version data pipelines using Airflow or similar orchestration tools
- Define alert routing, retry strategies, and backfill procedures for pipeline resilience
- Collaborate with Workflow Engineering on integration points between n8n and data pipelines
- Implement automated data validation including dbt tests, freshness checks, and anomaly detection for row counts
- Respond to data quality incidents within one business hour as per SLA requirements
- Document runbooks for recurring failure scenarios to streamline incident resolution
- Monitor, report, and reduce the frequency of data incidents on a quarterly basis
- Work with Workflow Automation Engineers to define ingestion contracts covering landing schemas and refresh schedules
- Collaborate with Junior AI Engineers to support RAG, embeddings, and AI services through feature tables and serving views
- Translate requirements from product management, customer success, and product teams into effective dimensional data models
Work Arrangement
Hybrid — Greater Toronto Area, Remote (if outside Greater Toronto Area)
Other
- Must be legally authorized to work in and based in Canada
- Due to the large volume of applications received, the company may use artificial intelligence to assist with screening
