Cloud is now one of the biggest business expenses and one of the hardest to manage.
At Finout, we’re not just shedding light on spend—we’re giving companies the power to make smarter, faster, and more strategic decisions about the cloud.
We’re trusted by brands like The New York Times, Wiz, Elastic, SiriusXM, and Lyft, and backed by top-tier investors with over $85M raised. In just 4 years, we’ve grown to 100+ people across Tel Aviv and New York—and we’re just getting started.
If you’re looking to build something big, solve real problems, and grow fast -we’d love to meet you.
As a Senior Software Engineer at Finout, you’ll play a key role in building and scaling the data infrastructure that powers our platform. You’ll work on complex, large-scale data systems that collect, process, and transform massive volumes of cloud and SaaS usage data into reliable, actionable insights for our customers.
You’ll take ownership of core data pipelines and backend services, tackle challenging problems in distributed systems, and drive improvements in performance, reliability, and scalability. Working closely with engineering teams and other business units, you’ll contribute to architectural decisions and help shape the next generation of Finout’s data platform.
This is a high-impact role for an engineer who enjoys solving complex data engineering challenges, working with modern cloud and big data technologies, and building resilient systems that operate at scale.
Responsibilities:
Own, enhance, and improve Finout’s core platform
Build and maintain systems for collecting and processing metrics from client environments
Focus on system efficiency and resilient infrastructure
Integrate well-known SaaS platforms into Finout’s big data repository (e.g., major cloud providers, Datadog, Snowflake)
Optimize Spark and Airflow processes
Contribute to data design and platform architecture while working closely with other business units and engineering teams
Face the challenges of testing and monitoring large-scale data pipelines
Requirements:
7+ years of experience developing and operating large-scale, high-availability systems
7+ years of experience with Python (Experience with Go is a plus)
Experience working with cloud environments (AWS preferred) and big data technologies (Spark, Airflow, S3, Snowflake, EMR)
Familiarity with metrics systems (e.g., Prometheus, cloud monitoring APIs) or time-series data – a strong plus
Autodidact, self-motivated team player with strong communication skills and a passion for solving challenges at scale
We're a hybrid company with a big vision and a startup soul. If you’re excited to help shape the future of cloud infrastructure and join a team that cares deeply about what (and how) we build—we’d love to meet you.
Skills Required
- 7+ years of experience developing and operating large-scale, high-availability systems
- 7+ years of experience with Python
- Experience working with cloud environments
- Experience with big data technologies (Spark, Airflow, S3, Snowflake, EMR)
What We Do
Finout is the enterprise-grade FinOps platform built for teams that need more than dashboards—they need action. From cloud to containers to SaaS and AI, Finout delivers business-aligned visibility and cost accountability without forcing engineers to tag everything or change how they ship code. At the core of our platform is the patented MegaBill engine with Instant Virtual Tags, enabling real-time, code-agnostic cost allocation across multi-cloud, Kubernetes, and third-party services. Whether you’re allocating Snowflake spend by feature, tracing GPU costs per AI model, or showing your CFO the cost-per-customer in Slack—we make it seamless. Finout empowers DevOps, FinOps, and platform teams to operationalize cloud cost data across the stack—embedding financial intelligence into CI/CD pipelines, tickets, reports, and forecasting workflows. Our unified approach helps organizations shift from reactive savings to proactive governance, bringing finance and engineering into strategic alignment. We’re not just showing what you spent—we’re showing why, where, and what to do about it.
