# About the Role

Navina is seeking a hands-on FinOps Lead to establish and manage its techno-financial operations practice across cloud, infrastructure, AI, data, and third-party engineering services.

The role will partner with Engineering, DevOps, AI Infrastructure, Product, Finance, and leadership to improve cost visibility, forecasting, optimization, and governance. The successful candidate will help engineering teams make cost-aware technical decisions while maintaining delivery speed, performance, and reliability.

# Key Responsibilities

– Build and own Navina’s FinOps and cloud cost management practice.
– Create transparent visibility into cloud and infrastructure spending.
– Lead cost-optimization initiatives from analysis and discovery through implementation and measurable results.
– Improve cost forecasting and develop COGS forecasts for engineering and leadership teams.
– Establish cost governance practices, including budgets, alerts, tagging, and cost allocation.
– Build dashboards, reports, and recurring cost-review processes.
– Analyze large datasets to identify cost drivers and provide actionable recommendations.
– Evaluate the impact of optimization initiatives on performance, reliability, and business outcomes.
– Translate financial insights into practical engineering solutions.
– Communicate technical and financial trade-offs clearly to cross-functional stakeholders.

# Required Skills

– 4+ years of experience in FinOps, Cloud Cost Management, Infrastructure Operations, DevOps, or a related field.
– 3+ years of software development experience in backend or data-focused environments.
– Hands-on experience managing and optimizing cloud spend in AWS or another major cloud provider.
– Strong understanding of cloud billing, tagging, cost allocation, budgets, alerts, reservations, savings plans, and usage-based cost models.
– Ability to understand technical architecture and assess cost-optimization opportunities.
– Experience building dashboards, reports, COGS forecasts, and recurring cost-review processes.
– Ability to analyze large datasets and communicate clear recommendations.
– Experience driving cost-optimization initiatives through execution and measurable impact.
– Strong collaboration skills with Engineering, Finance, Product, and leadership stakeholders.
– Strong ownership, independence, attention to detail, and communication skills.
– Ability to explain technical and financial trade-offs clearly.
– Collaborative approach and team-oriented mindset.

# Preferred Skills

– B.Sc. or M.Sc. in Computer Science or Software Engineering.
– Experience in a SaaS company with production-scale cloud infrastructure.
– Experience with Datadog and Snowflake.
– Experience with AI/ML infrastructure costs, data pipelines, or large-scale compute environments.
– Experience in a regulated industry such as healthcare, fintech, or enterprise SaaS.

# Cloud Platforms & Technologies

## Cloud Providers

– AWS
– Other major cloud providers

## Monitoring

– Datadog

## Data & Analytics

– Snowflake
– Data pipelines
– AI/ML infrastructure

# FinOps Responsibilities

– Manage and optimize cloud and infrastructure spend.
– Improve cost visibility across cloud, infrastructure, AI, data, and third-party engineering services.
– Develop COGS forecasts and improve forecasting accuracy.
– Establish budgets, alerts, tagging, cost allocation, and broader cost governance practices.
– Analyze cost drivers and usage-based spending models.
– Support reservations and savings-plan optimization.
– Build dashboards, financial reports, and recurring cost reviews.
– Evaluate optimization initiatives against performance, reliability, and business outcomes.
– Enable engineering teams to make informed, cost-aware technical decisions.

# Why You Might Be Interested

This role offers the opportunity to establish and lead a FinOps practice across cloud, infrastructure, AI, data, and third-party services. It combines software development, cloud economics, architecture, and cross-functional stakeholder management. The position also provides exposure to production-scale SaaS infrastructure and AI/ML cost environments within a digital health company.