Cloud FinOps Lead

About Kinaxis

Elevate your career journey by embracing a new challenge with Kinaxis. We are experts in tech, but it’s really our people who give us passion to always seek ways to do things better. As such, we’re serious about your career growth and professional development, because People matter at Kinaxis.

Location

Ottawa, Canada - Hybrid Toronto, Canada - Hybrid All other Canadian locations - Remote

About The Team

The Data & Analytics team drives Kinaxis’ transformation into a data-driven organization by building and governing scalable, modern data ecosystems. We deliver high-quality, reliable, and accessible data that empowers decision-making, improves operational efficiency, and enables new business opportunities. Our expertise spans data integration, engineering, governance, business intelligence, and cloud financial optimization.

We are seeking a highly skilled Cloud FinOps Lead to design, implement, and operate the financial governance, tooling, and processes that ensure Kinaxis’ cloud and AI spend is transparent, optimized, and aligned to business outcomes. You’ll partner closely with Data & Analytics, Cloud Operations, Cloud Engineering, Product Development, and Finance to drive cost efficiency, reduce waste, and enable scalable growth across our cloud infrastructure.

Salary Range

Annual cash compensation ranges from $118,400 to $162,800, plus a discretionary 12% company bonus. The final offer within this range will reflect the successful candidate’s skills and experience.

Vacancy Status

This is an existing job vacancy.

What you will do

• Build and operate FinOps capabilities: design, implement, and manage financial systems and processes for cloud cost management across the organization.
• Vendor funding & incentives optimization: proactively identify, track, and secure cloud vendor funding, incentives, credits, and subsidization opportunities; partner with relevant stakeholders to ensure programs are applied, realized, and measured.
• AI cost management: establish visibility and governance for AI/ML-related spend (e.g., model training/inference, GPU/accelerator usage, managed AI services), define cost controls, and drive optimization strategies.
• Cloud financial governance: define and evolve cost allocation, ownership, and governance models that align spend to products, teams, and environments.
• Tagging and allocation standards: own the cloud spend tagging policy framework, including standards, compliance monitoring, and continuous improvement.
• Commitment management: own and manage Kinaxis’ global committed use and reservations portfolio, balancing burn-down efficiency with budget constraints and operational needs.
• Licensing optimization & compliance: manage Windows Licensing footprint (SPLA, MPSA), optimize for cost savings while ensuring compliance; participate in audits as needed.
• Cross-functional optimization: collaborate with Cloud and Product teams to optimize customer-facing infrastructure spend and enable informed decision-making on cost/performance trade-offs.
• Reporting & insights: monitor and report on cloud and AI usage/cost trends; identify optimization opportunities; recommend, socialize, and present solutions to management.
• Stakeholder leadership: deliver impactful presentations and chair a monthly Steering Committee focused on cloud and AI spend, risks, and optimization outcomes.
• Product ownership mindset: act as a Product Owner within Data & Analytics to help build world-class visibility into cloud and AI spending (dashboards, metrics, alerts, and self-serve insights).
• Financial analysis & modeling: provide modeling support for cloud infrastructure investments, pricing/packaging implications, program initiatives, and capacity planning.
• Budgeting & forecasting: contribute to budgeting and forecasting processes for cloud and AI costs, including variance analysis and corrective action plans.
• Enablement & training: provide training, documentation, and support to teams on FinOps practices, tools, and decision frameworks.
• AI-enabled productivity: leverage AI tools to accelerate analysis, automate repetitive tasks, improve documentation quality, and increase day-to-day productivity (while ensuring responsible use of data and compliance with internal policies).
• Continuous improvement: stay current on FinOps and cloud/AI cost management best practices, vendor tooling, and emerging standards.
• Observability-driven optimization: leverage observability tooling (e.g., Datadog), including metrics, traces, and log data, to identify cost drivers, inefficiencies, over-provisioning, performance bottlenecks, and waste. Translate telemetry insights into actionable cost optimization recommendations.

What we are looking for

• Bachelor's degree