Staff Business Analytics Engineer

About this role

Are you energized by using data to uncover the operational and financial drivers behind a high-performing engineering organization? Do you enjoy breaking down complex technology costs, building unit economic and forecast models, and translating the results into decisions leaders can act on? Are you excited to shape how engineering teams measure performance, optimize investments, and balance cost, scale, and business impact? If so, we invite you to be a part of our innovative team.

As a Staff Business Analytics Engineer on Ridgeline’s Engineering Operations team, you’ll lead analytics and FinOps initiatives that give engineering leaders a clear view into the operational and financial performance of our technology organization. You’ll build the analytical foundation for understanding cost drivers, unit economics, cloud and infrastructure spend, forecasts, and engineering performance, connecting technical activity to meaningful business outcomes. As a strategic partner to Engineering, Finance, and other cross-functional leaders, you’ll turn complex data into trusted models and actionable insights that improve investment decisions, resource allocation, planning, and operational efficiency. You’ll work with cutting-edge technologies and AI tools to accelerate analysis, uncover insights, and increase the quality and efficiency of your work in a fast-moving, progressive environment.

At Ridgeline, how we work matters as much as what we build. Ridgeliners act like owners, choose growth over comfort, and communicate with transparency. We assume positive intent, bias toward action, and bring solutions—not just problems. We celebrate wins, learn from setbacks, and thrive in a resilient, collaborative, high-performing culture.If this excites you, we’d love to meet you!

You must be work authorized in the United States without the need for employer sponsorship.

The impact you will have:

• Own and evolve FinOps analytics that provide visibility into technology spend, cost drivers, utilization, efficiency, and opportunities for optimization

• Develop unit economic models that connect infrastructure and technology costs to meaningful product, customer, workload, and business drivers

• Build forecast models for cloud, infrastructure, tooling, and other engineering costs to improve planning, budgeting, and investment decisions

• Analyze actual performance against forecasts, identify the drivers of variance, and translate findings into actionable recommendations for Engineering and Finance leaders

• Establish scalable allocation and attribution methodologies that help teams understand the costs associated with products, services, environments, and workloads

• Partner with Engineering, Finance, and business leaders to evaluate tradeoffs between cost, performance, reliability, growth, and customer impact

• Define and evolve the metrics and analytical frameworks that help engineering leaders understand organizational performance and make informed decisions

• Build scalable data models, dashboards, and self-service analytics that create trusted visibility into engineering operations and financial performance

• Identify trends, anomalies, and optimization opportunities across operational and financial data and drive investigations from initial signal through recommendation

• Develop scenario and sensitivity analyses that help leaders understand the financial implications of changes in architecture, usage, growth, pricing, and engineering investments

• Establish data quality and validation practices that strengthen confidence in financial, operational, and engineering reporting

• Communicate complex financial and operational insights clearly to technical and non-technical audiences, influencing decisions through data and strong business judgment

• Drive ambiguous, high-impact analytical initiatives from problem definition through implementation, adoption, and measurable outcomes

• Contribute to a collaborative environment rooted in learning, teaching, experimentation, and continuous improvement

What we look for:

• Significant experience in business analytics, analytics engineering, FinOps, financial analytics, business intelligence, or a related discipline

• Demonstrated ability to lead complex, ambiguous analytics initiatives and influence strategic decisions across teams at a Staff level

• Experience analyzing cloud, infrastructure, technology, or other complex cost structures and identifying the underlying drivers of spend

• Experience developing financial forecasts, unit economic models, cost allocation methodologies, scenario analyses, or similar decision-support models

• Strong SQL skills and experience transforming complex datasets into reliable, reusable analytical models

• Strong financial and analytical acumen with the ability to connect technical and operational metrics to financial and business outcomes

• Experience developing dashboards, reporting systems, and self-service analytics that support operational and financial decision-making

• Ability to translate ambiguous business questions into measurable metrics, analytical approaches, and actionable recommendations

• Strong problem-solving skills and an aptitude for identifying patterns, investigating root causes, and navigating incomplete information

• Ability to communicate complex analytical and financial concepts clearly to technical and non-technical stakeholders

• Proven ability to build trusted partnerships and influence without authority across Engineering, Finance, and other functions

• A learning mindset and willingness to explore cutting-edge technologies and AI tools while cultivating deep expertise in engineering operations and FinOps

• Serious interest in having fun at work

Bonus :

• Experience with FinOps practices in a cloud-native or SaaS technology organization

• Experience analyzing AWS, Azure, GCP, Snowflake, Databricks, or other cloud and data infrastructure costs

• Familiarity with cloud cost a

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