Cloud & AI FinOps Engineer

The Hartford•Hartford, CT

2d•Hybrid

About The Position

The Enterprise Cloud Services team supports The Hartford’s multi-cloud transformation by providing a robust, optimized, self-service cloud experience that enables business value for our customers. The FinOps Center of Enablement partners across technology, finance, product, and business teams to strengthen financial accountability and maximize the value of cloud investments. The Cloud & AI FinOps Analyst is an onshore, hands-on role that blends cloud engineering knowledge, financial analysis, data analytics, automation, and stakeholder consulting. This individual will turn cloud and AI consumption data into actionable insights, improve forecasting and allocation, identify and help deliver optimization opportunities, and mature FinOps practices across the enterprise. The role partners with application owners, engineers, architects, product teams, finance, procurement, and leadership, with primary depth in AWS, strong working knowledge of GCP, and familiarity with Azure cost-management concepts.

Requirements

5+ years of experience in cloud engineering, architecture, operations, development, IT finance, business analytics, or FinOps, including substantial hands-on responsibility for AWS cost and usage management.
Demonstrated experience operating cloud financial management processes at enterprise scale, including allocation, budgeting, forecasting, anomaly management, optimization, and savings realization.
Strong knowledge of AWS pricing, billing, invoicing, cost allocation, and optimization mechanics, including AWS Organizations, consolidated billing, Cost Explorer, Cost and Usage Reports, Savings Plans, Reserved Instances, and AWS Marketplace.
Working knowledge of cloud architecture and cost drivers across compute, containers, databases, serverless, storage, data, networking, observability, and AI or machine-learning services.
Experience with cloud billing and cost management platforms; IBM Apptio Cloudability experience is strongly preferred.
Experience using SQL, Python, advanced Excel, or comparable tools to analyze large datasets, automate recurring work, and produce forecasts, recommendations, and financial models.
Working knowledge of finance, accounting, variance analysis, unit economics, and business analytics.
Ability to define and communicate showback or chargeback models, cost allocation methods, optimization strategies, and cost-performance-value tradeoffs to technical and non-technical audiences.
Ability to use AI-assisted tools responsibly for analysis and automation, validate generated insights, and work within enterprise security, privacy, data, and model-governance requirements.
Demonstrated ability to work independently, manage deliverables efficiently, influence without direct authority, and collaborate across engineering, finance, product, procurement, and business teams.
Strong written and verbal communication skills, sound judgment, attention to detail, intellectual curiosity, and a customer-focused mindset.

Nice To Haves

Experience with AWS, GCP (and some Azure) pricing, billing, reporting, commitment management, and optimization capabilities.
Experience with business intelligence and visualization platforms such as Microsoft Power BI, Tableau, or Google Looker.
Experience engineering repeatable data pipelines, APIs, notebooks, or automated workflows for cost ingestion, normalization, enrichment, reporting, and recommendation tracking.
Familiarity with the FinOps Open Cost and Usage Specification (FOCUS), cloud billing exports, data warehouses such as Snowflake, and integration with financial planning, CMDB, ITSM, or procurement systems.
Experience with infrastructure as code and policy-as-code tools such as Terraform or AWS CloudFormation and with integrating cost controls into engineering workflows.
Experience managing or analyzing AI and machine-learning costs, including GPU utilization, model or endpoint usage, token and inference economics, and vendor AI service pricing.
Knowledge of responsible AI, data governance, model governance, and human validation practices for AI-generated analysis or recommendations.
Experience in a mature Agile, product, platform engineering, or reliability engineering environment.
Relevant cloud certification, such as AWS Solutions Architect Associate, AWS Cloud Practitioner, Google Associate Cloud Engineer, or Microsoft Azure Fundamentals.

Responsibilities

Administer and improve cloud financial management platforms, including IBM Apptio Cloudability, AWS native cost tools, and applicable GCP or Azure capabilities.
Monitor cloud and AI cost and usage trends; investigate anomalies, billing discrepancies, unallocated spend, and material forecast variances; and coordinate root-cause analysis and corrective action.
Build a prioritized optimization pipeline covering rightsizing, idle-resource cleanup, storage lifecycle, workload scheduling, architecture changes, and service-selection tradeoffs; track recommendations through implementation and validate realized savings.
Analyze and manage commitment-based discount opportunities, including AWS Savings Plans and Reserved Instances, Spot, and GCP committed use discounts; monitor coverage, utilization, break-even points, and financial risk.
Partner with architects, cloud engineers, reliability engineers, developers, product teams, and application owners to embed cost, performance, reliability, and sustainability considerations into design and capacity decisions.
Develop cost estimates, unit-cost models, and scenarios for workloads migrating to, scaling within, or modernizing in the cloud.
Apply AI-assisted analytics and automation to anomaly detection, forecasting, recommendation prioritization, and narrative reporting, while validating outputs and maintaining appropriate data, security, and governance controls.
Support monthly, quarterly, and annual budget and forecast cycles for cloud and AI services, including consumption, show back or chargeback, savings, commitment, and demand planning.
Develop and improve trusted reports, dashboards, forecasts, alerts, and executive-ready insights using standardized cost and usage data.
Analyze large and complex datasets, build financial and unit-economics models, and translate technical cost drivers into clear recommendations and business tradeoffs.
Perform variance analysis and maintain month-end and quarter-end reporting, operational metrics, usage statistics, and KPI or OKR measures.
Establish and track metrics such as forecast accuracy, allocation coverage, commitment utilization, realized savings, optimization adoption, cost per transaction, cost per customer or product, and—where applicable—cost per token, inference, model, or AI workload.
Help cloud consumers understand their bills, cost drivers, efficiency trends, business impact, and options for aligning consumption with goals and budgets.
Produce concise management reporting that distinguishes identified, approved, implemented, and realized savings and clearly assigns ownership for follow-through.
Promote a FinOps culture of accountability, collaboration, transparency, and continuous improvement across distributed onshore and global teams.
Support the development, automation, integration, standardization, and governance of FinOps policies, processes, controls, and best practices.
Manage and improve tagging and labeling standards, cost allocation, account and project hierarchies, data quality controls, and policy enforcement.
Lead or participate in recurring business reviews, optimization forums, and knowledge-sharing sessions; communicate findings effectively to engineering, finance, product, business, and leadership audiences.
Provide consulting and training that advances FinOps capability maturity, self-service adoption, and team ownership of technology consumption.
Evaluate FinOps, observability, data, and AI technologies that can improve reporting, forecasting, automation, governance, and operational effectiveness.
Help extend FinOps practices beyond public cloud, where relevant, to AI platforms, SaaS, licensing, and other variable technology costs.
Develop guardrails for AI and machine-learning consumption, including visibility into GPU, API, token, model, and inference usage; alerting for abnormal consumption; and transparent allocation to products, teams, and business outcomes.
Create and maintain documentation, operating procedures, metric definitions, and standards; plan and execute work using Agile practices.
Perform other duties as assigned.