Data Engineer (Contract-Dec 2027)
At a glance
Mid-level Data Engineering role at Hdks. Waterloo · full-time · CA$92,000–CA$142,000 base.
dataroles summary, based on the employer's posting.
What you'll do
- Build pipelines that collect, clean, transform and store large datasets with BigQuery, Cloud Storage and Databricks
- Create and tune BigQuery warehouses and data models for reporting, business intelligence and machine learning
- Develop and maintain ETL or ELT workflows using Google Cloud tools
- Monitor pipeline and query performance, then improve speed and cost efficiency
- Work with data scientists, analysts and stakeholders to turn data needs into usable solutions
What you bring
- At least three years in hands-on data engineering, focused on Google Cloud pipeline development and data modeling
- Strong working knowledge of GCP data services, including BigQuery, Cloud Storage and Databricks
- Experience processing large datasets across distributed systems
- Practical skill with Airflow, Cloud Composer, Apache Beam and Python, plus advanced SQL and data modeling
Who this fits
This role suits a mid-level data engineer who enjoys building and improving data pipelines, models and analytics infrastructure. It is based in Waterloo, Canada, and is listed as a contract role ending in December 2027.
From the employer
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
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