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Dbt Data Engineer Jobs in Ontario (NOW HIRING)

A Sr. Data Engineer is sought to join the team. This individual will play a key role in evolving ... Airflow, dbt - Data storage and warehousing: PostgreSQL, Redshift, MongoDB (for unstructured data ...

This role will focus on modern data engineering practices, including Azure, Databricks, Unity Catalog, ETL/ELT pipeline development, dbt-based transformation, CI/CD automation, and data platform ...

... engineering initiatives ... Build and maintain dbt models across staging, mart, and metrics layers in the data warehouse ...

Data Engineer

Toronto, ON · Hybrid

CA$90K - CA$125K/yr

About The Role As a Data Engineer you'll be tasked with designing, building, and maintaining ... Hands-on experience building and maintaining transformation layers using dbt or similar ELT tools.

The engineer will follow end-to-end process standards and guidelines to ensure accurate and ... Build transformation models and data pipelines using dbt. * Develop optimized SQL transformations ...

Build and maintain dbt models across staging, mart, and metrics layers in the data warehouse ... to data engineering * Contribute to process improvements, documentation, and knowledge sharing ...

... DBT). · Expertise in Big Data Technologies (e.g., Spark, Hadoop). · Knowledge of Cloud Platforms (AWS, GCP, or Azure) and services like S3, Redshift, BigQuery,or Snowflake. · Experience with ...

As a Data Engineer at TheAppLabb, you will be responsible for designing, developing, and optimizing ... Airflow, DBT). Expertise in Big Data Technologies (e.g., Spark, Hadoop). Knowledge of Cloud ...

Lead, Data Engineer

Mississauga, ON · On-site +1

CA$122K - CA$162K/yr

Summary The Lead Data Engineer is a senior individual contributor within McKesson's Decision ... Apache Airflow, dbt, Alation * Strong understanding of data modeling, metadata, lineage, and data ...

Lead, Data Engineer

Mississauga, ON · On-site +1

CA$122K - CA$162K/yr

Summary The Lead Data Engineer is a senior individual contributor within McKesson's Decision ... Apache Airflow, dbt, Alation * Strong understanding of data modeling, metadata, lineage, and data ...

Undergraduate degree in software engineering, computer science or related field. * 7 years of experience with data engineering toolsets such as AirFlow, Argo, dbt, Nifi * 7 years of experience with ...

Data Engineer - student

Ottawa, ON · On-site

CA$55K - CA$60K/yr

Experience with data engineering techniques and toolsets such as Airflow, Argo, dbt, Nifi, Jupyter Notebook; * Experience with data modeling techniques and languages; * Experience with scripting ...

Manager, Data Engineering

Toronto, ON · Hybrid

CA$124K - CA$160K/yr

Hands-on expertise with modern data platforms (Snowflake, Databricks, DBT), data lakes, warehouses ... Programming and scripting proficiency in Python, Scala, or Java. * Demonstrated people leadership ...

We are seeking a Senior Data Engineer to help design and build the next generation of our Data ... Experience with semantic/metrics layers (Cube, dbt, Looker). * Familiarity with transformation ...

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Showing results 1-20

Dbt Data Engineer information

What is a dbt data engineer?

Dbt Data Engineers are professionals who specialize in using dbt (data build tool) to transform, test, and document data within modern data warehouses. They build and maintain data pipelines by writing SQL-based transformation scripts and ensuring data quality through automated testing. Dbt Data Engineers collaborate closely with analytics teams to create reliable, well-documented datasets that support business intelligence and analytics initiatives.

How does a dbt data engineer typically collaborate with data analysts and other stakeholders?

As a Dbt Data Engineer, you'll work closely with data analysts, business intelligence teams, and sometimes product managers to translate business requirements into reliable, well-structured data models. Collaboration often involves reviewing transformation logic, ensuring data quality, and providing documentation or training on Dbt models. You may also participate in regular stand-ups or data modeling sessions to align on priorities and address data challenges collaboratively. Effective communication skills are key, as you'll bridge the gap between raw data and actionable insights.

What are the key skills and qualifications needed to thrive as a dbt data engineer, and why are they important?

To thrive as a Dbt Data Engineer, you need strong SQL skills, experience in data modeling, and a solid understanding of ELT/ETL pipelines, often supported by a degree in computer science or a related field. Familiarity with dbt (data build tool), version control systems like Git, and cloud data platforms such as Snowflake or BigQuery is typically required. Attention to detail, problem-solving abilities, and effective collaboration are essential soft skills for this role. These skills ensure robust, scalable, and maintainable data transformations that drive reliable analytics and business insights.

What is the difference between Dbt Data Engineer vs Data Analyst?

AspectDbt Data EngineerData Analyst
Primary FocusBuilding and maintaining data transformation pipelines using dbtAnalyzing data to generate reports and insights
Skills & ToolsSQL, dbt, ETL pipelines, cloud platformsSQL, Excel, BI tools, data visualization
Work EnvironmentData engineering teams, cloud data platformsBusiness units, reporting teams
CertificationsSQL, cloud certifications, dbt trainingData analysis, visualization certifications

While both roles work with data and SQL, Dbt Data Engineers focus on developing scalable data transformation pipelines using dbt, whereas Data Analysts primarily analyze data to produce reports and insights. The roles complement each other within data teams but differ in technical scope and responsibilities.

What job categories do people searching Dbt Data Engineer jobs in Ontario look for?

The top searched job categories for Dbt Data Engineer jobs in Ontario are:

What cities in Ontario are hiring for Dbt Data Engineer jobs?

Cities in Ontario with the most Dbt Data Engineer job openings:

Infographic showing various Dbt Data Engineer job openings in Ontario as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution.

Senior Data Engineer

Rockstar

Toronto, ON • On-site

Full-time

Re-posted 23 days ago


Job description

Rockstar is recruiting for a fast-growing, mission-driven technology company focused on workforce development. The client is dedicated to building innovative digital solutions that empower individuals and organizations to thrive in the modern economy. Rockstar is supporting this client in their search for a talented Sr. Data Engineer to help evolve their core data platform and drive impactful business outcomes.

A Sr. Data Engineer is sought to join the team. This individual will play a key role in evolving the core data platform, which includes data pipelines, machine learning models, and various databases. The ideal candidate will combine technical data expertise with strong business intuition to build a foundation of reliable data. Expertise in data engineering will help build strong, performant pipelines.

What You'll Own

- Data infrastructure: Building and maintaining the infrastructure that powers the data platform including pipeline orchestration, data warehousing, and machine learning

- Data solutions that drive the product: Developing and maintaining data solutions alongside a team of data scientists that enable the product to function at scale and with quality

- Data governance and quality: Upholding best practices in data governance, ensuring accuracy, accessibility, and compliance across data systems

- Cross-platform data sourcing: Surfacing and integrating data from across the platform to address real business needs in product, engineering, and GTM

- Evolving core data models: Continuously evolving foundational models by identifying and incorporating new, high-value data sources

30/60/90 Day Plan

30 days:

- Onboarding/Learning Stack/Product

- Learning who the customers are, what their problems are, and how data can be leveraged to support them

- Gaining an understanding of core data entities and how they drive the product

- Contributing to core data pipelines by adding data quality and data enrichment layers

60 days:

- Working with data scientists to develop datasets and processes that streamline complex workflows

- Contributing to and owning aspects of the data catalog by defining and maintaining metrics, dimensions, and lineage

- Supporting surrounding teams in getting value out of the platform's data through regular reporting and analysis

90 days:

- Owning and automating reporting workflows from data ingestion all the way to building out dashboards and tools

- Independently gathering reporting and insights requirements from stakeholders

- Presenting findings to stakeholders and providing recommendations to drive the organization towards making data-driven decisions

Required Experience

- Proven ability to translate ambiguous business problems into clear, actionable insights

- Hands-on experience using SQL and Python for analysis in a professional setting

- Experience building and maintaining data pipelines, warehouses, and infrastructure

- Strong communication skills to convey technical insights to both technical and non-technical stakeholders

- Demonstrated ownership of analytics solutions, ensuring accuracy, reliability, and business alignment

- Familiarity with data visualization tools such as Looker, Power BI, or Tableau

- Familiarity with modeling structured and unstructured data, including NoSQL databases like MongoDB

- A sharp, kind, and open-minded approach, driven by both excellence and impact

Preferred Experience

- Hands-on experience with modern data tools like DBT and Airflow

- Experience with SageMaker or an equivalent machine learning / data science platform

- Experience in the workforce development industry

Our Tech Stack

- Languages: SQL, Python

- Data orchestration and transformation: Airflow, dbt

- Data storage and warehousing: PostgreSQL, Redshift, MongoDB (for unstructured data)

- Machine learning and experimentation: AWS SageMaker

- Visualization and reporting: Looker

- Infrastructure: AWS ecosystem (S3, Lambda, Glue, Redshift)