Job description:

We are seeking a Data Engineer to join our consulting team supporting a major telecom client. In this role, you will design and build scalable ingestion frameworks and high-throughput pipelines across Google Cloud Platform (GCP), bridging enterprise business intelligence, real-time telemetry, and emerging AI/ML initiatives.

Key Responsibilities:

- Design and deploy reliable batch and streaming data pipelines from diverse enterprise systems, telecom networks, and event streams into GCP.
- Architect dimensional models, data marts, and semantic layers using BigQuery and dbt to power executive reporting and operational BI.
- Collaborate with Data Science and ML engineering teams to build, validate, and orchestrate ML-ready feature stores and training datasets.
- Implement automated data quality frameworks, lineage tracking, and schema validation across the platform.
- Monitor, troubleshoot, and optimize query performance, storage costs, and compute resource allocation across Spark and BigQuery environments.

Required Qualifications:





- 3+ years of hands-on data engineering experience building production-grade data pipelines.
- Advanced proficiency in Python (or Scala) and expert-level SQL.
- Experience with core GCP data services: BigQuery Standard SQL, dbt, Cloud Composer/Airflow, and Dataflow pipelines
- Proven track record processing large-scale datasets using Apache Spark (PySpark or Spark SQL).
- Robust background in data modeling (Kimball/dimensional modeling, star/snowflake schemas) and transformation workflows using dbt.
- Experience designing robust ingestion patterns across relational databases, REST APIs, and message queues.

Preferred Qualifications:

- Prior industry experience within telecommunications, network operations, or high-volume event streaming environments.
- Exposure to MLOps patterns (e.g., Vertex AI, feature stores, model registry pipelines).
- Familiarity with enterprise BI platforms such as Looker or Power BI.

Pay: $45.00-$55.00 per hour

Expected hours: 37.5 per week

Work Location: Hybrid remote in Toronto, ON (Toronto District)

📌 Data Engineer (Toronto)
🏢 MSi
📍 Toronto