Technical Lead, Network Systems Data

Providius•Hamilton, ON

15h•Onsite

About The Position

Providius is looking for a Technical Lead, Network Systems & Data to help define, build, and validate the technical environments that support our next generation of network intelligence products. This is a hands-on technical role for someone who deeply understands networked systems, end devices, telemetry, configuration, and operational behaviour. This role will help determine what systems need to be built, what data needs to be collected, what scenarios need to be tested, and how technical observations should be translated into useful product requirements. You will work closely with engineering, product leadership, and customer-facing teams. A major part of the role will be building practical test environments, identifying meaningful system behaviours, shaping usable datasets, defining operational scenarios, and helping the team understand what is technically real, measurable, and valuable. The ideal candidate can move comfortably between hands-on technical work and product-level thinking. You should be able to configure systems, reason through network behaviour, identify useful telemetry, explain tradeoffs clearly, and help turn technical findings into product direction for real customer environments. This role is well suited to someone with experience in networking, infrastructure, media systems, AV-over-IP, observability, industrial systems, or other complex technical environments where reliability, visibility, and system behaviour matter.

Requirements

5+ years of experience in network engineering, systems engineering, solutions architecture, technical product work, infrastructure engineering, observability, or a closely related technical role.
Strong hands-on understanding of networking concepts, including switching, routing, VLANs, multicast, traffic flows, packet behaviour, and network troubleshooting.
Experience working with networked devices, infrastructure systems, telemetry, logs, configurations, monitoring tools, or packet/flow data.
Ability to reason about system behaviour, operational state, failure modes, data quality, and edge cases.
Ability to translate ambiguous technical goals into concrete environments, scenarios, requirements, and execution plans.
Comfort working with engineering teams and communicating technical findings clearly.
Strong practical judgment, including the ability to distinguish between what is theoretically possible, what is measurable, and what is useful to customers.
Ability to work independently, ask good questions, organize ambiguity, and maintain forward progress without a fully defined roadmap.
Strong written and verbal communication skills.

Nice To Haves

Experience working with data science, machine learning, anomaly detection, or advanced analytics teams.
Experience defining datasets, labeling strategies, telemetry schemas, test scenarios, or validation criteria.
Experience with network automation, scripting, Python, APIs, or data processing tools.
Experience with cloud-connected and on-premise deployment models.
Experience in smaller teams, early-stage product development, or environments where requirements are still being shaped.
Familiarity with agile delivery, backlog management, or product discovery practices.

Responsibilities

Define, build, and maintain representative networked test environments for product development, validation, and data collection.
Work hands-on with switches, endpoints, networked devices, telemetry sources, configuration, traffic flows, packet captures, and system behaviour.
Identify what data should be collected from devices, networks, applications, configurations, logs, telemetry streams, and operational events.
Help define realistic operational scenarios, including normal behaviour, configuration changes, degraded performance, device changes, timing issues, unexpected traffic patterns, and customer-specific use cases.
Work with engineering and machine learning teams to turn real system behaviour into usable datasets, labels, feature requirements, test cases, and validation criteria.
Investigate how different customer environments, device types, configurations, deployment models, and operational constraints may affect product behaviour and data requirements.
Translate technical findings into clear requirements and recommendations for engineering, product leadership, and commercial teams.
Help product and customer-facing teams understand what is technically possible, what is measurable, what is valuable, and what should be prioritized.
Identify risks, assumptions, edge cases, and technical constraints early, and communicate them clearly.
Support customer discovery, pilots, lab demonstrations, and field validation where technical depth is required.
Maintain practical documentation of environments, scenarios, data sources, observations, assumptions, and requirements.