Cloud Data Engineer responsible for modern Data & AI solutions on Microsoft Azure. Collaborating with clients and teams to develop production - ready data platforms and support analytics.
About the role
- Data Engineer responsible for building and maintaining data pipelines and datasets for enterprise analytics in the mining industry. Collaborates with Project Manager and technical teams for data solutions.
Responsibilities
- 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.
- 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.
Requirements
- Bachelor’s degree in Computer Science, Software/Data Engineering, Information Systems, or a related field; equivalent practical experience considered.
- 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.
- 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.
Benefits
- Overtime may be required to meet project deadlines
- International travel as required for the purpose of meeting with clients, stakeholders, or off-site personnel/management.
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