Data Engineer

Salary Range: $70,000.00 To $ Annually
At Mac Lean Engineering, we're not just building machines - we're redefining the future of mining through innovation. Inspired by over 50 years of pioneering excellence, Mac Lean Engineering continues to lead the way in underground mining equipment solutions. Rooted in our founder's unwavering commitment to safety, productivity, and enduring quality, we are driven by innovation and expansion. With facilities strategically located across Ontario in Collingwood, Owen Sound, Barrie, and Sudbury, and across the globe in Mexico, South Africa, and Australia, our dedicated workforce collaborates in a safety centric environment to deliver superior components and equipment to the underground mining, municipal, environmental, and industrial sectors.
The Position  We're seeking a Data Engineer to join a new Data team building the pipelines and datasets that turn telematics and operational data from Mac Lean's vehicles into trusted analytics and customer-facing products. Reporting to the Team Lead - Data, you'll be a hands-on contributor on the migration from our legacy telemetry platform to a modern cloud data warehouse, and on the streaming pipeline that lands next-generation field data from connected vehicles.
You'll work across the full ingestion-to-consumption path: change-data-capture from legacy relational systems into the cloud warehouse, streaming consumers landing high-frequency vehicle data, and curated transforms that unify historical, batch, and streaming sources into a single schema defined by our vehicle data semantics contract. Downstream, your datasets power Mac Lean's commercial telemetry products: fleet utilization reporting, fault and signal-health alerting, OEM KPI dashboards, and a roadmap toward predictive maintenance and anomaly detection.
This role sits within the Telematics, Automation, and Controls Division and partners closely with the Platform team, the Vehicle Interface team, and product stakeholders. You'll write production Go and SQL day to day, operate at the intersection of industrial IoT and modern data platforms, and ship across Mac Lean's 32+ vehicle types and global footprint.
Responsibilities and Duties   Build and operate ingestion pipelines from vehicles and legacy systems into the cloud warehouse - including change-data-capture from relational sources, streaming consumers writing high-frequency telemetry, and refactoring existing batch ingestion onto the new platform.
Develop and maintain curated transforms that unify historical, batch, and streaming telemetry sources into a single schema aligned to our vehicle data semantics contract.
Model data in the warehouse across raw, curated, and semantic layers, and build the certified datasets that downstream dashboards, alerts, and analytics products consume.
Build the data flows behind Mac Lean's commercial telemetry products - utilization reporting, hour meter-based dashboards, fault and signal-health alerting, OEM KPI dashboards, operator and shift analysis, and near-real-time data delivery for streaming use cases.
Implement schema management, data contracts, data quality checks, and SLA monitoring across pipelines and datasets, including dual-read and dual-write validation during migration cutovers.
Write production Go services and SQL transforms; contribute Python where appropriate for analytics tooling and ML feature pipelines.
Partner with the Platform team on the messaging subject namespace, message envelope standards, and stream provisioning - consuming the platform's governance model rather than defining it, and surfacing data-side requirements back to it.
Partner with the Vehicle Interface team on signal definitions, since the vehicle data semantics contract is the structural source of truth for curated datasets.
Support the data science workstream by building reliable feature datasets for predictive maintenance, root cause diagnostics, and anomaly detection, and by collaborating on production model pipelines.
Contribute to the evaluation and integration of a modern observability and analytics platform against curated datasets, and help migrate existing portal use cases onto it.
Follow team engineering standards for code quality, testing, CI/CD, containerized deployment (Docker/Kubernetes), and release governance for pipelines, datasets, and models.
Qualifications  The ideal candidate will possess strong communication skills, leadership abilities, and problem-solving capabilities. They will demonstrate…