job details
We are seeking a highly skilled Data Engineer for a permanent role to lead the design and implementation of robust data pipelines and advanced analytics models. In this position, you will be the backbone of our data initiatives, ensuring that internal and external datasets are securely extracted, transformed, and curated to support Artificial Intelligence (AI) and Business Intelligence (BI) across the enterprise. You will bridge the gap between raw data sources and actionable insights by building high-performance semantic layers and feature pipelines. ...
Type: Permanent / Full-Time
Location: Etobicoke/Toronto Area
Rate: Competitive
Advantages
Leading-Edge Tech: Work at the intersection of Data Engineering and Artificial Intelligence, building the infrastructure that drives modern innovation.
Strategic Impact: Influence the enterprise data architecture and governance standards for a major organization.
Professional Stability: Enjoy the benefits of a permanent role with a focus on long-term career growth and complex problem-solving.
Collaborative Culture: Work in an environment that values independent judgment, technical excellence, and cross-functional teamwork.
Responsibilities
Pipeline Engineering: Design, build, and maintain scalable ETL/ELT pipelines using tools like Fivetran and DBT to integrate diverse internal and external data sources into analytic databases.
Data Modeling: Model databases for efficient storage and retrieval, ensuring the integration of complex datasets aligns with enterprise architecture standards.
AI/BI Readiness: Prepare curated tables, semantic layers, and feature pipelines specifically designed for machine learning models and executive reporting.
Quality & Governance: Establish data quality metrics, perform profiling and cleansing, and collaborate with business units to remediate data integrity issues.
Architecture & Lineage: Contribute to the development of enterprise data architecture and meticulously document data lineage to ensure compliance and transparency.
Security & Privacy: Support critical initiatives related to data classification, loss prevention, and privacy standards to protect the organization's digital assets.
Collaboration: Provide expert guidance to peers and external partners on reporting standards, data organization, and governance.
Qualifications
Education: Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Data Science, or a related quantitative field.
Experience: Minimum of 4 years in a quantitative or analytics role with a proven track record of managing complex data environments.
Technical Stack: Proficient in Python, SQL, and Microsoft Azure, with hands-on experience in DBT, Fivetran, Knime, or Dataiku.
Database Mastery: High proficiency in SQL and experience with at least two relational database management systems (RDBMS).
Analytics Readiness: Demonstrated experience in feature engineering and semantic modeling for AI/BI applications.
Programming: Familiarity with scripting languages such as R and VBA in addition to Python.
Governance Knowledge: Solid understanding of data privacy, classification policies, and data governance frameworks.
Communication: Strong ability to translate complex technical pipeline concepts into clear, actionable information for non-technical stakeholders.
Summary
If you are a technical expert who thrives on building elegant data solutions and preparing organizations for the future of AI, we encourage you to apply today!
This posting is for existing and upcoming vacancies.
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We are seeking a highly skilled Data Engineer for a permanent role to lead the design and implementation of robust data pipelines and advanced analytics models. In this position, you will be the backbone of our data initiatives, ensuring that internal and external datasets are securely extracted, transformed, and curated to support Artificial Intelligence (AI) and Business Intelligence (BI) across the enterprise. You will bridge the gap between raw data sources and actionable insights by building high-performance semantic layers and feature pipelines.
Type: Permanent / Full-Time
Location: Etobicoke/Toronto Area
Rate: Competitive
Advantages
Leading-Edge Tech: Work at the intersection of Data Engineering and Artificial Intelligence, building the infrastructure that drives modern innovation.
Strategic Impact: Influence the enterprise data architecture and governance standards for a major organization.
Professional Stability: Enjoy the benefits of a permanent role with a focus on long-term career growth and complex problem-solving.
Collaborative Culture: Work in an environment that values independent judgment, technical excellence, and cross-functional teamwork. ...
Responsibilities
Pipeline Engineering: Design, build, and maintain scalable ETL/ELT pipelines using tools like Fivetran and DBT to integrate diverse internal and external data sources into analytic databases.
Data Modeling: Model databases for efficient storage and retrieval, ensuring the integration of complex datasets aligns with enterprise architecture standards.
AI/BI Readiness: Prepare curated tables, semantic layers, and feature pipelines specifically designed for machine learning models and executive reporting.
Quality & Governance: Establish data quality metrics, perform profiling and cleansing, and collaborate with business units to remediate data integrity issues.
Architecture & Lineage: Contribute to the development of enterprise data architecture and meticulously document data lineage to ensure compliance and transparency.
Security & Privacy: Support critical initiatives related to data classification, loss prevention, and privacy standards to protect the organization's digital assets.
Collaboration: Provide expert guidance to peers and external partners on reporting standards, data organization, and governance.
Qualifications
Education: Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Data Science, or a related quantitative field.
Experience: Minimum of 4 years in a quantitative or analytics role with a proven track record of managing complex data environments.
Technical Stack: Proficient in Python, SQL, and Microsoft Azure, with hands-on experience in DBT, Fivetran, Knime, or Dataiku.
Database Mastery: High proficiency in SQL and experience with at least two relational database management systems (RDBMS).
Analytics Readiness: Demonstrated experience in feature engineering and semantic modeling for AI/BI applications.
Programming: Familiarity with scripting languages such as R and VBA in addition to Python.
Governance Knowledge: Solid understanding of data privacy, classification policies, and data governance frameworks.
Communication: Strong ability to translate complex technical pipeline concepts into clear, actionable information for non-technical stakeholders.
Summary
If you are a technical expert who thrives on building elegant data solutions and preparing organizations for the future of AI, we encourage you to apply today!
