Job Overview
Looking for someone who has a passion for cloud financial management, cost optimization, and building automation at scale? We are seeking a Fin Ops Engineer to join our team and lead the implementation and operation of Fin Ops practices with a focus on Microsoft Azure. This role is ideal for someone with a strong cloud/infrastructure engineering background who also brings strong development and data engineering skills.
You will work with large datasets, APIs, and automation tools to support Fin Ops processes and contribute to the development of internal tooling that functions similarly to a SaaS application. In this role, you will collaborate with engineering, finance, and product teams to drive cost efficiency, transparency, and financial accountability in cloud operations.
Responsibilities will include:
Analyze Azure cloud usage and spend to identify and implement cost-saving opportunities, including rightsizing, reservations, and eliminating waste.
Build and maintain ETL pipelines using Python to collect, transform, and aggregate Azure cost and usage data from multiple sources.
Develop and maintain Grafana dashboards that provide actionable insights into cloud spend, usage trends, and optimization opportunities.
Work with the Fin Ops Product Owner to define, estimate, and deliver backlog items in Azure Dev Ops, contributing technical expertise to product planning.
Automate routine Fin Ops tasks such as tagging enforcement, anomaly detection, and cost allocations.
Partner with engineering, finance, and procurement teams to support budgeting, forecasting, and chargeback processes.
Create and maintain documentation for Fin Ops processes, tools, and best practices.
Advocate for Fin Ops best practices across the organization, helping teams understand the financial impact of their cloud usage.
Support governance and policy enforcement to ensure cloud resources are compliant with organizational standards.
Qualifications
Education:
Skills:
CPUS Engineering employs artificial intelligence (AI) tools as part of its recruitment process to enhance efficiency and consistency. These tools may assist with activities such as application screening, candidate evaluation, and summarization of interview feedback.
AI-generated outputs are intended to supplement, not replace, human judgment. All final hiring decisions are made by qualified human decision-makers who exercise professional discretion in reviewing and assessing candidate information alongside any AI-generated insights.
CPUS Engineering ensures that the use of AI in recruitment complies with all applicable employment laws, human rights legislation, and privacy regulations. The organization is committed to maintaining fairness, transparency, and non-discrimination throughout the hiring process.
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