Lead Software Engineer – FinOps – Salesforce

Job Details

About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

The Salesforce Cloud Economics and Capacity Management (CECM) team is looking for a Lead Software Engineer with experience across distributed systems to join us!

You will be working cross-functionally with engineers, FinOps analysts, and product managers to build the breakthrough features that our internal customers will love, adopt and use while ensuring stable and scalable applications. You’ll be a part of a modern, lean, self-governing FinOps product engineering team where you have the ability to switch hats between coding to requirements gathering, to testing for quality and performance.

CECM develops intelligent, data-driven tools which enable strategic financial operations (FinOps) decision-making pertaining to Salesforce infrastructure expenditure and capacity management. We are building a platform that provides near real-time monitoring of cloud cost and capacity utilization of the infrastructure, which will help in optimizing resource allocation, forecasting spend, and minimizing costs. We incorporate advanced machine learning techniques to turn the petabytes of data generated by our global infrastructure into actionable predictions and business insights used by FinOps practitioners, internal service owners, and technical leaders daily. As an internal tooling team, engineers are expected to directly interact with customers to develop requirements and design, release and maintain distributed systems with visibility throughout Salesforce.

This is a fantastic opportunity for someone who is passionate about building scalable, resilient, distributed systems that collect, process, and analyze massive volumes of cloud cost and usage data. The skillset requires strong data architecture, ETL, SQL and a proven track record working with enterprise metrics to build automated data pipelines. Broad experience in distributed backend development is preferred.

Responsibilities

• Develop, automate, enhance, maintain scalable ETL pipelines
• Develop and maintain dashboards and reports to visualize cloud cost and usage trends
• Collaborate with finance and engineering teams to understand and manage cloud spending
• Automate cost anomaly detection, alerting, forecasting, budget process, and other FinOps capabilities
• Independently design and develop resilient and reusable automation data frameworks
• Lead distributed backend development pipeline development being delivered by multiple engineers (predominantly Python)
• Lead and participate in requirement gathering, design, and development of complex datasets
• Mentor team members in all aspects of the data maturity lifecycle
• Work and collaborate with global teams across AMER and APAC regions

Required Skills/Experience

• Bachelor’s degree in Computer Science or equivalent industry experience
• 7+ years of experience in data engineering, preferably in cloud or FinOps environments
• Experience in distributed SQL analytics engines such as Spark and Trino, and cloud cost management platforms
• Deep expertise in building ETL pipelines using tools like dbt, Airflow, or similar orchestration frameworks
• Understanding of cloud cost management principles and FinOps methodologies
• Strong communication and stakeholder management skills
• Experience working in Agile and Scrum methodology, incremental delivery, and CI/CD
• Experience in at least one cloud service providers including AWS or GCP
• Experience in distributed backend software engineering environments
• Experience with data visualization tools, preferably Tableau