Job Description

CBTS serves enterprise and midmarket clients in all industries across the United States and Canada. CBTS combines deep technical expertise with a full suite of flexible technology solutions--including Application Modernization, Managed Hybrid Cloud, Cybersecurity, Unified Communications, and Infrastructure solutions. From developing and deploying modern applications and the secure, scalable platforms on which they run, to managing, monitoring, and optimizing their operations, CBTS delivers comprehensive technology solutions for its clients' transformative business initiatives. For more information, please visit www.cbts.com

OnX is a leading technology solution provider that serves businesses, healthcare organizations, and government agencies across Canada. OnX combines deep technical expertise with a full suite of flexible technology solutions—including Generative AI, Application Modernization, Managed Hybrid Cloud, Cybersecurity, Unified Communications, and Infrastructure solutions. From developing and deploying modern applications and the secure, scalable platforms on which they run, to managing, monitoring, and optimizing their operations, OnX delivers comprehensive technology solutions for its clients’ transformative business initiatives. For more information, please visit www.onx.com

LEAD ENGINEER – DATA ENGINEERING

JOB TITLE: Lead Engineer – Data Engineering

1.Role Purpose (1–3 lines): Design, build, and optimize large-scale data pipelines and integration frameworks that support analytics, reporting, and AI/ML workloads. This role ensures high data availability, quality, and performance across enterprise systems.

2.Key Responsibilities:

•Develop, maintain, and enhance scalable data ingestion, transformation, and storage pipelines.

•Ensure data integrity, reliability, and performance across distributed systems and databases.

•Collaborate with data architects, analysts, and business stakeholders to translate requirements into robust engineering solutions.

•Implement best practices for data quality, lineage, and governance within engineering workflows.

•Optimize data processing for performance, scalability, and cost efficiency.

•Lead troubleshooting and resolution of production data issues to ensure minimal downtime.

•Mentor and guide engineers through the processes
Key Performance Indicators (KPIs):

•Uptime and reliability of data pipelines and processing systems.

•Mean time to detect and resolve (MTTR) data pipeline incidents or failures.

•Data accuracy, completeness, and consistency across systems.

•Efficiency and scalability of ETL/ELT processes.

•SLA adherence for data availability and processing timelines.

•Compliance with data governance and security requirements.

•Reduction in manual interventions through automation.

•Reusability and standardization of engineering components and frameworks.

Qualifications & Experience:

·Degree: Graduate / Professional Degree in Computer Science, Information Technology, Data Science, Engineering, or related field

·Years of experience: Graduate – 8 to 10 years

·Relevant experience: 7 years +
Key Interfaces:

·Internal: Collaborates with data architects, analysts, data scientists, and DevOps teams for seamless data delivery and integration.

·External: Engages with client IT, AI/ML team, cloud platform providers, technology vendors, and implementation partners for tooling, optimization, and platform enhancements.