Data Engineer (Contract-Dec 2027)
Waterloo, ONT, Canada
- Pay
$92,000–142,000/yearAnnual period assumed — pay source
Solid understanding of Agentic AI Salary Range: $92,000-$142,000
Read the full posting- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s listing: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and implement data pipelines using BigQuery, Cloud Storage and Databricks
Build and optimize data warehouses on Google BigQuery for analysis and reporting
Design scalable data models for data products, BI and machine learning
Develop and maintain ETL/ELT processes using GCP tools
Monitor and optimize pipelines and queries for performance and cost
From the employer’s posting
Design and implement robust data pipelines to collect, clean, transform, and store large volumes of data using GCP services like BigQuery and Cloud Storage, as well as DataBricks
Build and optimize data warehouses on Google BigQuery for efficient data analysis and reporting.
Design and implement scalable data models to support strategic data products, business intelligence, and machine learning applications.
Develop and maintain Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes using GCP tools.
Continuously monitor and optimize data pipelines and queries for performance and cost-effectiveness.
What you’ll bring
All qualificationsCore experience
- Experience with Dataflow is an asset.
- Bachelor's or Master's degree in Math, Computer Science, Software Engineering, or a related field.
- Experience with large-scale data processing and distributed systems.
- Proven ability to develop dynamic Airflow DAGs and robust data solutions that support efficient, reliable data processing.
- Solid understanding of Agentic AI
Qualification wording
Google Cloud Platform (GCP): Strong proficiency in GCP data services, including BigQuery, Cloud Storage, and DataBricks. Experience with Dataflow is an asset.
Bachelor's or Master's degree in Math, Computer Science, Software Engineering, or a related field.
Experience with large-scale data processing and distributed systems.
Proven ability to develop dynamic Airflow DAGs and robust data solutions that support efficient, reliable data processing.
Solid understanding of Agentic AI
Education & alternatives
- Google Cloud Platform (GCP): Strong proficiency in GCP data services, including BigQuery, Cloud Storage, and DataBricks. Experience with Dataflow is an asset. - Bachelor's or Master's degree in Math, Computer Science, Software Engineering, or a related field. - Minimum 3-5+ years of experience in a hands-on data engineering role, with a focus on pipeline development and data modeling on Google Cloud Platform.
Tools in this posting
- SQL
- BigQuery
- Databricks
- GCP
- Airflow
- Python
Source — Tool mentions in context
- Strong data engineering expertise, building and orchestrating scalable data pipelines using Airflow, Cloud Composer, Apache Beam, and Python. - Advanced SQL and data modeling skills, including complex query development, optimization, schema design, and data analysis. - Proven ability to develop dynamic Airflow DAGs and robust data solutions that support efficient, reliable data processing.
What to Expect - Data Ingestion and Processing: Design and implement robust data pipelines to collect, clean, transform, and store large volumes of data using GCP services like BigQuery and Cloud Storage, as well as DataBricks - Data Warehousing and Analytics: Build and optimize data warehouses on Google BigQuery for efficient data analysis and reporting.
- Data Ingestion and Processing: Design and implement robust data pipelines to collect, clean, transform, and store large volumes of data using GCP services like BigQuery and Cloud Storage, as well as DataBricks - Data Warehousing and Analytics: Build and optimize data warehouses on Google BigQuery for efficient data analysis and reporting. - Data Modeling: Design and implement scalable data models to support strategic data products, business intelligence, and machine learning applications.
What you Bring: - Google Cloud Platform (GCP): Strong proficiency in GCP data services, including BigQuery, Cloud Storage, and DataBricks. Experience with Dataflow is an asset. - Bachelor's or Master's degree in Math, Computer Science, Software Engineering, or a related field.
- Bachelor's or Master's degree in Math, Computer Science, Software Engineering, or a related field. - Minimum 3-5+ years of experience in a hands-on data engineering role, with a focus on pipeline development and data modeling on Google Cloud Platform. - Experience with large-scale data processing and distributed systems.
- Experience with large-scale data processing and distributed systems. - Google Cloud Professional Data Engineer Certification or equivalent is a strong asset for this role. - Strong data engineering expertise, building and orchestrating scalable data pipelines using Airflow, Cloud Composer, Apache Beam, and Python.
- Google Cloud Professional Data Engineer Certification or equivalent is a strong asset for this role. - Strong data engineering expertise, building and orchestrating scalable data pipelines using Airflow, Cloud Composer, Apache Beam, and Python. - Advanced SQL and data modeling skills, including complex query development, optimization, schema design, and data analysis.
- Advanced SQL and data modeling skills, including complex query development, optimization, schema design, and data analysis. - Proven ability to develop dynamic Airflow DAGs and robust data solutions that support efficient, reliable data processing. - Solid understanding of Agentic AI
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 5, 2026 · 21 days ago
- Recorded sightings
- 30
- Last seen by us
- Oct 26, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
Your next step
- Have your resume and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
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Job description
The Opportunity
The Data Engineer is responsible for designing, building, and maintaining scalable data pipelines and data assets that support data-driven decision making across the organization. Working closely with cross-functional data teams, they integrate new data sources into existing data products while leveraging GCP technologies to manage and process large-scale datasets. The role focuses on developing reusable, configurable, and efficient data pipeline frameworks, as well as producing clean, modular, and well-documented code that enhances collaboration, reduces operational overhead, and accelerates future development.
What to Expect
- Data Ingestion and Processing: Design and implement robust data pipelines to collect, clean, transform, and store large volumes of data using GCP services like BigQuery and Cloud Storage, as well as DataBricks
- Data Warehousing and Analytics: Build and optimize data warehouses on Google BigQuery for efficient data analysis and reporting.
- Data Modeling: Design and implement scalable data models to support strategic data products, business intelligence, and machine learning applications.
- ETL/ELT Development: Develop and maintain Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT) processes using GCP tools.
- Performance Optimization: Continuously monitor and optimize data pipelines and queries for performance and cost-effectiveness.
- Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver data solutions.
What you Bring:
- Google Cloud Platform (GCP): Strong proficiency in GCP data services, including BigQuery, Cloud Storage, and DataBricks. Experience with Dataflow is an asset.
- Bachelor's or Master's degree in Math, Computer Science, Software Engineering, or a related field.
- Minimum 3-5+ years of experience in a hands-on data engineering role, with a focus on pipeline development and data modeling on Google Cloud Platform.
- Experience with large-scale data processing and distributed systems.
- Google Cloud Professional Data Engineer Certification or equivalent is a strong asset for this role.
- Strong data engineering expertise, building and orchestrating scalable data pipelines using Airflow, Cloud Composer, Apache Beam, and Python.
- Advanced SQL and data modeling skills, including complex query development, optimization, schema design, and data analysis.
- Proven ability to develop dynamic Airflow DAGs and robust data solutions that support efficient, reliable data processing.
- Solid understanding of Agentic AI
Salary Range: $92,000-$142,000
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Solid understanding of Agentic AI Salary Range: $92,000-$142,000
- Location & working pattern
Waterloo, ONT, Canada
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
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