Job Description:

Our client FinOps Team is seeking a product-oriented FinOps engineer to define and deliver capabilities that help Digital Product Teams (DPTs) understand, influence, and optimize the cost of technology decisions before spending occurs. The role sits at the intersection of FinOps Engineering, Cloud Platform, Finance, and Data Analytics, combining product ownership, engineering curiosity, financial insight, and systems thinking.

This role will make FinOps practical and consumable for engineers and product teams by delivering products, capabilities, and experiences for workload cost attribution, optimization recommendations, forecasting, AI and tokenomics governance, and product-level linkage between technology consumption and business outcomes.

Key Responsibilities:

Product Ownership & Outcome Delivery:

• Contribute to the FinOps product vision, roadmap, backlog, and measurable outcomes across DPTs, translating strategy into prioritized capabilities and user experiences.
• Partner with product, engineering, platform, finance, architecture, and data stakeholders to identify user needs, define requirements, manage tradeoffs, and drive adoption.
• Define success measures for FinOps capabilities, including adoption, forecast accuracy, unit-cost improvement, optimization realization, and engineering engagement.

Unit Economics & Business Value:

• Assist application areas define and operationalize unit economics such as cost per quote, claim, policy, transaction, workload, customer interaction, AI inference, or other business-relevant unit.
• Connect technology consumption and product costs to business outcomes through consistent allocation models, product metadata, and value-oriented KPIs.
• Create frameworks and reusable data products that enable DPTs to compare unit-cost trends, understand cost drivers, and evaluate architectural or product decisions.

AI Cost Management & Tokenomics:

• Collaborate with stakeholders to establish AI cost governance across model usage, token consumption, inference, training, fine-tuning, agentic workflows, and supporting data or platform services.
• Develop tokenomics and AI unit-cost measures, including cost per token, request, inference, user interaction, agent task, or business outcome.
• Enable model-selection economics by evaluating cost, performance, quality, latency, capacity, and business-value tradeoffs.
• Drive optimization opportunities through prompt efficiency, context and cache management, right-sized model selection, capacity planning, and managed commitment strategies.

Cloud, Container & Workload Optimization:

• Deliver actionable intelligence on utilization, cost trends, anomalies, and optimization opportunities across AWS, Azure, GCP, and hybrid environments.
• Optimize containerized workloads by improving Kubernetes cluster efficiency, node and pod rightsizing, autoscaling, scheduling, idle-capacity management, storage and network consumption, and shared-cost allocation.
• Identify and facilitate savings through workload modernization, rightsizing, scheduling, licensing strategies, commitment programs, reservations, and vendor negotiations.
• Oversee cloud billing and enterprise discount programs, including Savings Plans, Reserved Instances, reservations, and other commitment-based constructs.

Engineering Enablement & Shift-Left FinOps:

• Embed cost insight and policy into engineering workflows such as CI/CD, Terraform, Infrastructure as Code, observability platforms, planning tools, and developer portals.
• Enable teams to estimate and compare cost before deployment, establish cost guardrails, and evaluate financial impact during design and architecture decisions.
• Partner with engineering teams to design cost-efficient cloud, container, AI, and hybrid solutions without compromising reliability, security, or performance objectives.

Data, Analytics & Forecasting:

• Integrate cloud cost and usage data, observability telemetry, CMDB records, product metadata, financial systems, and business outcome datasets.
• Define data models, allocation logic, governance controls, and enterprise data-quality practices required for trustworthy FinOps insights.
• Develop forecasts, scenarios, anomaly detection, dashboards, and recommendations that support product planning, capacity decisions, and financial accountability.
• Configure and manage FinOps and analytical platforms to provide scalable, automated, and role-based experiences.

Governance, Transparency & Enablement:

• Lead advisory sessions and enablement with engineering, finance, product, and business stakeholders to align technical decisions with financial and business objectives.
• Create standards and governance that encourage accountability while preserving team autonomy and delivery speed.

Qualifications:

• Product ownership or product management experience delivering technical, data, platform, FinOps, or technology financial management capabilities.
• Strong knowledge of cloud cost management, allocation, showback or chargeback, forecasting, optimization, and financial governance across AWS, Azure, and GCP.
• Demonstrated ability to define and operationalize unit economics that connect technology consumption to product or business outcomes.
• Experience with AI cost governance, token monitoring, model-selection economics, inference cost, prompt optimization, AI capacity planning, or commitment management.
• Experience optimizing containerized and Kubernetes-based workloads, including rightsizing, autoscaling, cluster efficiency, shared-cost allocation, and observability.
• Experience integrating FinOps insights into Datadog or similar observability platforms, CI/CD pipelines, Terraform or other IaC workflows, and planning tools.
• Experience with data integration, modeling, analytics platforms, data governance, and enterprise data-quality practices.
• Ability to work with cloud billing and usage data, telemetry, CMDBs, product metadata, financial records, and business outcome datasets.
• Proficiency in automation and analytical methods using APIs, Terraform, Python, SQL, Excel, BI tools, and cost-management platforms such as Cloudability, CloudHealth, Adaptive6 or FinOut.
• Knowledge of enterprise discount programs, reservations, commitment plans, licensing optimization, and vendor commercial constructs.
• Excellent communication, stakeholder influence, prioritization, and outcome-based delivery skills across technical and financial audiences.
• Sustified Practitioner, FinOps Certified Engineer, and TBM certification are highly desirable.

Salary/Rate: $75-$90/HR (depends on experience level). This is a contract position with candidates expected to work 40 hours/ week.

About Us

Peterson Technology Partners (PTP) is an Equal Opportunity Employer committed to creating a transparent, inclusive, and human-centered hiring experience.

For more than 28 years, PTP has operated as one of the top IT staffing and recruiting firms in the USA—built on trust, long-term partnerships, and technical excellence.

Based in the Chicago suburb of Park Ridge, IL, our team of more than 500 employees and consultants is dedicated to:

• Helping every client make the best hiring decisions possible
• Matching professionals with the right IT jobs and career opportunities

As part of that commitment, we believe in providing clear information about how our hiring technologies work and how your data is used. The following section outlines our AI-assisted interview process and your rights as a candidate.

AI-Assisted Interview Experience (Pete & Gabi – Rebecca):

To provide a consistent, fair, and flexible experience for all candidates, we use AI-assisted tools to support parts of the interview process. This includes our proprietary AI platform Pete & Gabi, which includes AI recruiter Rebecca.

These AI hiring tools help us:

• Conduct recorded video interviews
• Transcribe interviews
• Summarize candidate responses
• Generate job-related insights
• Streamline communication and scheduling

Please note that:

• The AI does NOT make hiring decisions; all decisions are made by our human recruiters, hiring managers, or client partners.
• The AI does not evaluate facial expressions, emotions, or physical traits; it is used only to support fairness, consistency, and efficiency.

If you prefer a non-AI interview format, we will gladly provide an alternative.