# Analytics Engineer – Cloud & FinOps

## About the Role

This role focuses on improving visibility into cloud spend, infrastructure usage, and platform activity across a large-scale multi-cloud environment. The goal is to connect billing data, telemetry, and business metrics so engineering and platform teams can better understand the drivers of cost and efficiency.

You will work closely with Cloud Platform Engineering, SRE, and Product teams to build datasets, analytical models, and automated insights that support data-driven decisions. The position is based in Bangalore (Bellandur) and follows a mandatory hybrid schedule with three days in the office.

## Key Responsibilities

– Analyze cloud usage and spend across multiple providers to identify cost drivers, trends, and optimization opportunities.
– Build and maintain data pipelines and transformations for billing exports, infrastructure telemetry, operational metrics, and internal business systems.
– Develop scalable datasets and data models for cost analysis, infrastructure utilization, capacity planning, and operational monitoring.
– Investigate cloud cost anomalies and infrastructure changes to identify root causes and improvement areas.
– Improve data accuracy, consistency, and governance across analytical datasets.
– Translate complex infrastructure data into clear insights and recommendations for engineering and leadership stakeholders.
– Support FinOps initiatives by improving visibility into how workloads and engineering decisions affect cloud spend.
– Use AI-assisted tools to accelerate exploration, analysis, investigation, and automated detection of anomalies or behavior changes.

## Required Skills

– Strong SQL and data modeling skills.
– Proficiency in Python for analysis and automation.
– Experience with modern data platforms such as Redshift, BigQuery, or Snowflake.
– Experience with analytics and visualization tools such as Tableau, Looker, Power BI, or QuickSight.
– Experience working with large-scale operational or infrastructure datasets.
– Experience using AI-assisted development tools such as Cursor, Claude, Copilot, or similar.
– Strong analytical and problem-solving ability.
– Ability to turn complex infrastructure data into actionable insights.
– Comfort working in a cross-functional engineering environment.

## Preferred Skills

– Experience with cloud infrastructure data in AWS, Azure, or GCP.
– Familiarity with cloud billing datasets and cost management analytics.
– Experience analyzing large-scale infrastructure or telemetry data.
– Background in FinOps, cloud operations, platform engineering, or infrastructure analytics.
– Familiarity with Kubernetes or distributed systems environments.
– Experience with AI-driven analytics tools and LLM-based analysis.

## Cloud Platforms & Technologies

– **Cloud Providers:** AWS, Azure, GCP
– **Data Platforms / Warehouses:** Redshift, BigQuery, Snowflake
– **BI / Visualization:** Tableau, Looker, Power BI, QuickSight
– **Programming Languages:** SQL, Python
– **AI-Assisted Tools:** Cursor, Claude, Copilot
– **Data Sources:** cloud billing exports, infrastructure telemetry, operational metrics, internal product and business systems
– **Infrastructure / Orchestration:** Kubernetes, distributed systems environments

## FinOps Responsibilities

– Analyze cloud cost and usage data across multiple providers to identify spend drivers and optimization opportunities.
– Improve visibility into how platform workloads and engineering decisions influence cloud spend.
– Investigate cost anomalies and infrastructure changes to determine root causes.
– Build analytical frameworks for cost allocation, unit economics, infrastructure utilization, capacity planning, and operational performance monitoring.
– Develop automated workflows that help teams detect anomalies, usage trends, and system behavior changes more efficiently.

## Why You Might Be Interested

This is a hands-on analytics role at the intersection of cloud operations, FinOps, and data engineering. It offers the chance to work with large-scale multi-cloud data and build models and pipelines that improve visibility into spend and efficiency. You will collaborate with Cloud Platform Engineering, SRE, and Product teams, and use AI-assisted analytics methods to speed up investigation and insight generation.