Senior Analytics Engineer
GoBolt View all jobs
- Toronto, ON
- $100,000-140,000 per year
- Permanent
- Full-time
- Analytics-as-Code (dbt): Lead the architecture and evolution of our dbt project. You will design and maintain modular, version-controlled transformations, utilizing advanced dbt features (macros, packages, and custom tests) to ensure our Star Schema is the gold standard for logistics data.
- Dimensional Modeling: Design and implement robust data warehouse structures (Kimball/Inmon). You'll bridge the gap between raw backend events and the semantic layer, ensuring our data reflects the complex reality of last-mile delivery.
- Full-Stack Data Engineering: Own the "T" in ELT. You'll use Python to build custom extractors for APIs and bridge gaps in our ingestion layer, ensuring data flows seamlessly into our warehouse (BigQuery/Snowflake).
- The Semantic Layer & BI Performance: Optimize the interface between our warehouse and BI tools (Looker/Metabase).
- Reliability & CI/CD: Implement and advocate for software engineering best practices within analytics. This includes managing CI/CD pipelines for data, enforcing documentation-as-code, and building automated data quality monitoring to catch anomalies before they reach the stakeholders
- Experience: 3 to 5 years in Analytics Engineering or Data Engineering, specifically focused on building production-grade data pipelines.
- The "Product" Mindset: You treat data models like software. You care about modularity, dry code, and technical debt. You don't just fix a bug; you write a test to ensure it never happens again.
- Technical Stack:
- dbt Specialist: You are a dbt power user. You understand snapshots, incremental models, and how to optimize a DAG for cost and speed.
- SQL Architect: Beyond "Advanced SQL"-you understand execution plans, partitioning, and how to write code that scales with billions of rows.
- Python Proficiency: You're comfortable writing scripts for data manipulation (Pandas/Polars) and interacting with REST APIs to pull niche logistics data.
- Modeling Expert: You can debate the merits of a bridge table vs. a flattened view and know exactly how to model a complex "Order Life Cycle."
- Version Control: Git is your second language. You are comfortable with branching strategies, PR reviews, and collaborative development.
- Logistics DNA: Prior experience with supply chain data (last-mile delivery, warehouse management systems, or routing logic).
- Cloud Infrastructure: Hands-on experience with GCP (BigQuery) and orchestration tools like Airflow or Dagster
- Certifications: dbt Analytics Engineering Certification is a massive plus