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JFrog is seeking a Senior Analytics Engineer – Cloud & FinOps to help drive data-driven decision making across our cloud infrastructure and platform operations. This role will focus on building analytics solutions that provide visibility into cloud costs, infrastructure utilization, and platform usage patterns. The engineer will develop data pipelines, analytical models, and automated insights that connect cloud billing data, infrastructure telemetry, and business metrics. Working closely with Cloud Platform Engineering, SRE, and Product teams, this role will help the organization better understand how infrastructure usage and engineering decisions impact cloud spend and operational efficiency. Our platform operates in a large-scale multi-cloud environment, generating significant volumes of infrastructure and operational data. The ideal candidate enjoys working with complex datasets and building analytical systems that help engineering teams understand cost drivers, infrastructure behavior, and system efficiency. In addition, the role is expected to leverage modern AI-driven analytics tools and models (e.g., LLM-based analysis, automated insight generation, and intelligent data exploration) to enhance data processing, pattern detection, and decision support capabilities. Experience working with such tools is highly valued.
Job Responsibility:
Analyze cloud cost and usage data across multiple cloud providers to identify cost drivers, trends, and optimization opportunities
Develop analytical frameworks that connect infrastructure usage, operational metrics, and business activity
Support FinOps initiatives by improving visibility into how platform workloads and engineering decisions influence cloud spend
Investigate cloud cost anomalies and infrastructure changes to identify root causes and improvement opportunities
Design and maintain data pipelines and transformations that ingest data from sources such as: cloud billing exports
infrastructure telemetry
operational metrics
internal product and business systems
Build scalable analytics datasets and data models optimized for infrastructure and cost analysis
Ensure data accuracy, consistency, and governance across analytical datasets
Analyze large datasets to identify relationships between system behavior, infrastructure utilization, and cloud cost patterns
Develop analytical frameworks supporting: cloud cost allocation and unit economics
infrastructure utilization analysis
capacity planning
operational performance monitoring
Translate complex datasets into clear insights and recommendations for engineering and leadership teams
Leverage modern AI-assisted tools to accelerate data exploration, analysis, and investigation
Build automated workflows that help teams identify cost anomalies, usage trends, and system behavior changes more efficiently
Requirements:
Strong SQL and data modeling expertise
Python for data analysis and automation
Experience building data pipelines and ETL/ELT workflows
Strong analytical thinking and problem-solving skills
Experience working with large-scale operational or infrastructure dataset
Experience using AI-assisted development tools such as Cursor, Claude, Copilot, or similar
Experience working with modern data platforms such as: Redshift, BigQuery, Snowflake
Experience with analytics and visualization tools such as: Tableau / Looker / Power BI / QuickSight
Experience working with cloud infrastructure data (AWS, Azure, or GCP)
Familiarity with cloud billing datasets and cost management analytics
Experience analyzing large-scale infrastructure or telemetry data
Experience working in FinOps, cloud operations, platform engineering, or infrastructure analytics environments
Familiarity with Kubernetes or distributed systems environments
Strong analytical and problem-solving abilities
Ability to translate complex infrastructure data into clear and actionable insights
Comfortable working in cross-functional engineering environment