Int. Data Engineer to build and model secure, governed data pipelines on GCP (BigQuery, Airflow)
- Full-time
Our financial services client is seeking a Int. Data Engineer (5-7+ years) to build and model secure, governed data pipelines using GCP, BigQuery, and Apache Airflow
Join a data modernization initiative within a leading Canadian digital banking environment focused on scalable enterprise data solutions. This role supports the design and development of data pipelines, data warehouses, data models, and data products using GCP, BigQuery, Apache Airflow, Informatica, Python, SQL, and Git. The position also contributes to data governance, data quality, security, privacy, and SDLC activities across enterprise data platforms. Experience with data mesh architecture and modern data warehouse setup is considered valuable for the team.
Contract, Toronto, Working Hours: EST
4-days on-site Downtown Toronto
Must Haves
- 5-7+ years in Data Engineering, data management solution design, and development
- Hands-on delivery using GCP, BigQuery, Apache Airflow, Informatica, Python, SQL, and Git
- Strong background in Data Modeling, dimensional modeling, ETL/ELT, and Data Warehouse development
- Implementation of Data Governance, metadata, lineage, data quality, security, privacy, and access control
- University or College degree in a relevant field, or equivalent professional experience
Nice to Have
- Financial services background with retail banking exposure
- Data mesh architecture and modern data warehouse setup
- Data product development experience
- SDLC and release management exposure
Responsibilities
- Design and build scalable data pipelines, data warehouses, data models, and data products
- Develop cloud-based data solutions using GCP, BigQuery, Apache Airflow, Informatica, Python, and SQL
- Design conceptual, logical, physical, and dimensional data models
- Build ETL/ELT processes and optimize data transformation workloads
- Apply data governance, metadata, lineage, data quality, security, privacy, and access-control principles
- Troubleshoot data and technical issues and recommend solutions
- Produce technical documentation covering data models, data flows, designs, and operational processes