Humber On Data architect & software developer — azure / Jobs – 4 Openings This Week

Data Engineer
Redpath Mining —Vaughan, ON
- $100,000–$140,000 a year
- Overtime
- Azure
- Computer Science
- Engineering
Full Stack Developer
Magna International —Brampton, ON
- From $100,000 a year
- Full-time
- Azure
- Computer Science
- DevOps
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Data Team Lead
Redpath Mining —Vaughan, ON
- Overtime
- Azure
- Computer Science
- DevOps

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Data Engineer

Redpath Mining -
Vaughan, ON

Apply Now

Job details

Full-time
$100,000–$140,000 a year

Qualifications

Power BI
Azure
Computer Science
Engineering
Spark
Git
SQL
Bachelor's
Usability
Metadata
Python

Full job description

The Data Engineer is a core member of the Connected Data team, responsible for building and maintaining data pipelines and datasets that support enterprise reporting and analytics.

Working within a project-based delivery model, this role contributes to the incremental development of a unified data platform by integrating data from enterprise and operational systems into usable, structured datasets. The role operates in an evolving environment where data availability, definitions, and priorities may change, requiring adaptability and a strong delivery focus.

The Data Engineer works closely with the Project Manager, Data Architect, and Power BI Developers to deliver data solutions aligned with Connected Data priorities.

Salary Range - 100,000 - 140,000

Duties and Responsibilities:

Work under the direction of the Project Manager to align implementation activities with project priorities, timelines, and milestones.
Collaborate with the Project Manager on planning, sequencing, and estimation of technical work, providing input on scope, risks, and dependencies.
Support a phased, use-case-driven delivery approach by balancing sound engineering practices with timely execution.
Contribute to the implementation of data architecture, including data models, integration patterns, and data flows aligned with established and evolving design.
Translate business requirements into practical data structures and transformations with guidance from senior team members.
Apply and follow established standards for data modeling, integration, and engineering practices.
Contribute hands-on to pipeline and data model implementation to support early delivery and validate design approaches.
Ensure solutions consider performance, reliability, and cost efficiency.
Design, build, and maintain data ingestion and transformation pipelines from enterprise and operational systems.
Contribute to development of datasets that support prioritized reporting use cases (e.g., earned vs burned, productivity, equipment utilization).
Work within a prioritized backlog to deliver incremental data capabilities aligned to project milestones.
Take ownership of specific pipelines or data domains, ensuring reliability and maintainability.
Support implementation of data governance practices, including data quality, metadata, lineage, and access control.
Apply established data models, naming conventions, and standards to ensure consistency and reuse.
Contribute to master data alignment across key domains (e.g., projects, equipment, locations) in collaboration with business stakeholders.
Ensure adherence to organizational security, privacy, and compliance requirements in delivered solutions.
Work with incomplete, inconsistent, or evolving data sources and contribute to improving data quality over time
Support testing, validation, and monitoring of data pipelines
Identify issues and propose practical solutions to improve reliability and usability of data
Work with business stakeholders to understand reporting needs and translate them into clear technical requirements.
Engage stakeholders in coordination with the Project Manager to align technical delivery with business priorities.
Participate in design reviews, working sessions, and demonstrations to validate solutions and gather feedback.
Support documentation of data structures, transformations, and usage to enable adoption.
Maintain confidentiality with respect to Redpath business and vendor information
Support other members of the Corporate IT teams as required
The duties and responsibilities listed above are representative of the nature and level of work assigned and are not necessarily all inclusive

Education:

Bachelor’s degree in Computer Science, Software/Data Engineering, Information Systems, or a related field; equivalent practical experience considered.
Relevant certifications (e.g., Azure, Data Engineering, Analytics) are an asset but not required where strong hands-on experience is demonstrated.

Experience:

4–8+ years of hands-on experience building and maintaining data pipelines, integrations, or analytical datasets.
Experience contributing to data delivery across multiple stages, including requirements understanding, implementation, and support.
Experience working with structured and semi-structured data from multiple sources.
Demonstrated ability to work in delivery-focused environments with evolving requirements, imperfect data, and tight timelines.
Experience supporting or contributing to reporting datasets (e.g., Power BI semantic models or equivalent) is an asset.
Exposure to asset-intensive industries (e.g., mining, construction, utilities) or operational data domains is an asset but not required.
Experience working within cross-functional teams, collaborating with business stakeholders and technical team members.

Technical Skills:

Proficiency in SQL and data transformation concepts; experience with tools such as Spark, Python, or similar is an asset.
Experience working with modern data platforms (e.g., Microsoft Fabric, Azure Data Factory, Azure Databricks or similar), including data ingestion, transformation, and storage concepts.
Familiarity with building and supporting reporting datasets (e.g., Power BI semantic models), including basic modeling and performance considerations.
Exposure to data ingestion patterns (batch and/or near real-time) is an asset.
Experience integrating data from multiple systems (e.g., ERP, project controls, HSE, or similar) is an asset.
Understanding of data governance concepts, including data quality, access control, and basic metadata practices.
Familiarity with version control (e.g., Git) and structured development practices.

Core Competencies:

Strong problem-solving skills and attention to detail.
Ability to work effectively in fast-paced, evolving environments.
Clear communication with both technical and non-technical stakeholders.
Ownership mindset and willingness to learn and grow.
Commitment to safety, quality, and ethical conduct.

Additional Information:

Overtime may be required to meet project deadlines

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