Senior Lead Infrastructure Engineer - AWM FinOps Engineer

JPMorgan Chase & Co.; Columbus (OH); On-site; USD 120,000 - 180,000; Full time

Job summary

JPMorgan Chase & Co. is seeking a FinOps Engineer to optimize cloud spend across AWS and private cloud platforms. You will work hands-on on container platforms, automation, and rightsizing strategies to drive measurable savings.

Collaborate with engineering, finance, and product teams to translate cost data into actionable work and monitor progress against budgets and forecasts.

Qualifications

• Bachelor’s in CS/CIS/Engineering (or equivalent practical experience).
• Strong AWS engineering fundamentals with cost awareness; ability to translate usage into actionable engineering changes.
• AWS platform depth: Aurora PostgreSQL; ECS on Fargate; Karpenter or comparable autoscaling concepts.
• Infrastructure as Code / Dev tools: Terraform; GIT workflows; Bitbucket; IDE proficiency.
• CI/CD & deployment: Harness or Jenkins pipelines; Spinnaker knowledge.
• Programming/automation: Python; JavaScript; building automation with production practices.
• Financial literacy: Budgets, variance analysis, forecasting/outlooks; quantify waste and savings.
• Communication: clear explanation of technical + financial concepts to engineering, finance, and product stakeholders.

Responsibilities

• Translate cost and usage data into actionable engineering work and track results against budgets and realized savings.
• Detect waste and drive remediation (rightsizing, scheduling, elastic scaling).
• Collaborate with engineering, finance, and product stakeholders to quantify savings and communicate results.

Education

Bachelor’s in CS/CIS/Engineering

Job description

Join a high-performing, agile FinOps Engineering team focused on building the technical capabilities that optimize cloud and platform spend and improve value realization. You will work hands-on across AWS and private cloud infrastructure, container platforms, and automation to detect waste, drive remediation (rightsizing, scheduling, elastic scaling), and deliver measurable savings. You’ll partner closely with engineering, finance, and product stakeholders to translate cost and usage data into actionable engineering work and track results against budgets, forecasts, and realized savings.

Required qualifications, capabilities, and skills:

• Education/Experience: Bachelor’s in CS/CIS/Engineering (or equivalent practical experience).
• Core technical (FinOps Engineering): Strong AWS engineering fundamentals with cost awareness (billing concepts, tagging/allocation basics, cost drivers, cost attribution); ability to translate usage into actionable engineering changes.
• AWS platform depth (must have): Aurora PostgreSQL; ECS on Fargate (task sizing, autoscaling, scheduling/capacity-provider strategy); Karpenter (or comparable Kubernetes cluster autoscaling concepts).
• Infrastructure as Code / Dev tools: Terraform; GIT workflows (branching, PRs, code review); Bitbucket; IDE proficiency.
• CI/CD & deployment: Harness or Jenkins pipelines; Spinnaker (working knowledge or experience).
• Programming/automation: Python; JavaScript; ability to build/maintain automation with production practices (tests, logging, observability, documentation).
• Financial literacy (must have): Budgets, variance analysis, forecasting/outlooks; ability to quantify waste, estimate expected savings, implement remediation, and measure realized savings.
• Communication: Strong written/verbal communication; able to explain technical + financial concepts clearly to engineering, finance, and product stakeholders; able to provide resolution recommendations when inefficiencies are identified.

Preferred qualifications, capabilities, and skills:

• FinOps specialization: Experience building or operating cost optimization capabilities (spend anomaly detection, waste detection, rightsizing, scheduling automation, showback/chargeback, unit cost modeling).
• LLM/automation: Prompt engineering and/or building LLM-enabled workflows/skills integrated with engineering systems and cost data.
• Forecasting: Practical application of time series or driver-based forecasting methods for cloud/platform spend.
• Containers level cost attribution: Familiarity with workload cost allocation/attribution approaches and telemetry (labels/tags discipline, metrics-based attribution).