MacLean Engineering is looking to add a skilled and experienced Team Lead Data
and Industrial Streaming to join our growing team in Cambridge, Ontario. If you are looking to be challenged, valued, and rewarded with a competitive compensation package in a dynamic work environment, this position is for you!
The Position: We're seeking a Team Lead - Data to stand up and grow a new Data team that will turn telematics and operational data from MacLean's vehicles into trusted datasets, predictive insights, and customer-facing analytics. If you're an experienced data engineering leader who wants to build a capability from the ground up - setting the architecture, hiring and mentoring the team, and shipping real products - this role is for you.
As the leader of the Data team within the Telematics, Automation, and Controls Division, you'll define the data strategy, architecture, and delivery roadmap spanning ingestion from vehicles and legacy systems, streaming and storage, governed semantic layers, BI and operational reporting, and machine learning products such as predictive maintenance, root cause diagnostics, anomaly detection, and fleet optimization. You'll own the end-to-end data platform - pipelines, datasets, semantic layer, and data APIs - that moves data from the edge to curated, governed datasets and on to analytics and ML consumers, while partnering cross-functionally with vehicle interface, platform, autonomy, controls, and business stakeholders.
You'll lead a multidisciplinary team spanning analytics, data engineering, and data science - building a culture of engineering rigor, data quality, and business impact. The successful candidate will operate at the intersection of industrial IoT and modern data platforms, designing resilient ingestion pipelines, robust schema and contract management, and production ML workflows that scale across MacLean's 32+ vehicle types and global footprint.
Responsibilities and Duties: - Lead and grow a multidisciplinary Data team spanning analytics, data engineering, and data science, providing hands-on mentorship in modern data platform practices and setting direction for the function.
- Define and own the end-to-end data architecture (pipelines, datasets, semantic layer, and data APIs - running on infrastructure provided by the Platform team): vehicle data acquisition (OPC UA), external integrations (MQTT, Sparkplug B, PI, and similar), streaming, raw 12 curated 12 semantic storage layers, and external APIs for data consumers.
- Oversee ingestion from vehicles and CDC from legacy systems, ensuring reliable, low-latency movement of data from the edge into curated, governed datasets.
- Establish and enforce schema management, data contracts, data quality checks, and SLAs across pipelines and datasets.
- Partner cross-functionally with vehicle interface, platform, autonomy, controls, and business stakeholders to translate operational needs into trusted datasets, KPIs, dashboards, and analytics products.
- Drive the BI and reporting practice: KPI definition with business stakeholders, certified datasets and metric governance, operational and executive dashboards, and self-service enablement for business users.
- Drive the data science practice: predictive maintenance, root cause diagnostics, telemetry anomaly detection, fleet optimization, and customer-facing analytics features, with rigorous experimentation and model evaluation.
- Collaborate with the Platform team on MLOps, deploying and monitoring models in production and ensuring reproducibility across training and serving.
- Own data governance, security, and compliance on the data platform, including access control, lineage, auditability, and cybersecurity practices for data at rest and in motion.
- Partner with on-vehicle software and edge-platform teams to define and enforce strict edge-to-cloud data contracts and schema registries.
- Set engineering standards for the team - code quality, testing, CI/CD, containerized deployment (Docker/Kubernetes), and release governance for pipelines, datasets, and models.
- Help to manage delivery: roadmap, prioritization, hiring, performance, and stakeholder communication, ensuring the team ships measurable business impact.
Qualifications: The ideal candidate will possess strong communication skills, leadership abilities, and problem-solving capabilities. They will demonstrate organizational skills, discipline, an aptitude for proactive learning, and a positive attitude. Additionally, they should meet the following qualifications:
- Minimum 7 years of experience in data engineering or a closely related data discipline, including time spent leading or mentoring a team.
- Proven track record as a data engineering practitioner - designing and operating production ingestion, streaming, and storage systems at scale.
- Bachelor's degree or higher in computer science, software engineering, data engineering, or a related field.
- Hands-on experience building streaming and ingestion pipelines using technologies such as Kafka, RabbitMQ, 0MQ or NATS, and with industrial or IoT protocols such as OPC UA and MQTT. 0202 Strong programming skills in Go, Python, and SQL; experience with MLflow or equivalent model lifecycle tools is an asset.
- Deep experience with modern cloud data warehouses (e.g., Snowflake) and with raw 12 curated 12 semantic data modeling.
- Experience with schema management, data contracts, CDC from legacy systems, data quality frameworks, and SLA-driven operations.
- Experience with containerized deployment and orchestration (Docker, Kubernetes).
- Working knowledge of the analytics stack used by BI consumers (e.g., Power BI, Grafana/Observe) and of the data science stack (e.g., Azure Machine Learning or equivalent, MLflow, PyTorch or TensorFlow), sufficient to lead and review the work of analysts and data scientists.
- Partner with on-vehicle software (Controls/VMS) and edge-platform teams to define and enforce strict edge-to-cloud data contracts and schema registries
- Experience partnering with business stakeholders on KPI definition, metric governance, and executive reporting.
- Strong verbal and written communication skills in English.
- Proficiency in Spanish is a strong asset given cross-site collaboration with Mexico.
- Experience in mining, industrial, or heavy-equipment telematics is an asset.
- Exposure to MLOps practices and production ML deployment is an asset.
We offer competitive wages and benefits including:
- 3 weeks' vacation annually
- Paid sick and family emergency time
- Free coffee!
- Flexible Group benefits Package (Once probationary period is completed)
- Employee and family assistance program
- Voluntary DPSP (Deferred Profit-Sharing Plan) and GRRSP (Group Registered Retirement Savings Plan) (Once probationary period is completed)
- Annual Employee Bonus
- $250 annual safety shoe allowance
- $200 bi-annual prescription safety glasses allowance
- Employee appreciation days, raffles/draws, BBQ's and much more
If you are interested in being a part of an established and growing Canadian Company, we want to hear from you!Please note that employment with MacLean Engineering is contingent upon the successful completion of a background check conducted by our third-party provider, Sterling Backcheck. This process may include verification of employment history, education, and a criminal record check, depending on the requirements of the position.
We thank all applicants in advance for their application and interest in MacLean Engineering & Marketing Co Limited. However, only those candidates selected for an interview will be contacted. At times, AI-assisted tools may be used as part of the application screening process
This is an onsite position 5 days per week and can be in Sudbury, Collingwood, or Cambridge facilities. Regional Premiums may apply based on location.
