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About the Role

As a Data Platform FinOps Specialist, you will drive the FinOps & Cost Optimisation function for data workloads across BigQuery, Databricks, Azure, and Snowflake for over 70 business units. You will build cost-visibility dashboards, operate the monthly FinOps review cycle, implement GCP committed-use discount strategies, and translate spend data into actionable optimisation recommendations to reduce platform unit costs.

Responsibilities

Build and maintain cost visibility dashboards covering BigQuery, Databricks DBU, and Snowflake credit consumption, broken down by BU, team, project, and workload.
Own the spend attribution model, implementing chargeback and showback frameworks for BU finance teams.
Identify and act on GCP CUD optimisation, analyzing slot commitment vs. on-demand patterns and recommending right-sizing and reservation changes.
Run monthly unused-asset cleanup cycles to identify and coordinate decommission of idle datasets, dormant pipelines, and over-provisioned clusters.
Operate the monthly FinOps review with BU finance stakeholders, preparing spend summaries, variance analysis, and savings tracking.
Instrument GCP billing APIs and Dataplex cost metadata to automate cost tagging and anomaly alerting.
Partner with the Platform Engineering team to embed FinOps guardrails in CI/CD pipeline templates.
Contribute to the platform unit economics model, including cost per query, cost per pipeline run, and cost per BU workspace.

Requirements

Bachelor's Degree (BS) in Computer Science, Finance, Engineering, or a related field, or equivalent education and experience (7 years or more).
Proficient in cloud FinOps, cloud billing analysis, or data platform cost management (4 years or more).
Proficient with GCP billing, including billing export to BigQuery, CUD/SUD mechanics, budget alerts, recommender API, and the Cloud Billing API (4 years or more).
Proficient in SQL for writing complex billing queries and building dashboards from BigQuery billing export data (4 years or more).
Proficient in building cost visibility dashboards using Looker Studio, Grafana, or equivalent BI tooling (3 years or more).
Proficient in designing and presenting chargeback and showback models to finance and BU leadership (3 years or more).
Proficient in Japanese at a business level (3 years or more).
Proficient in English at a professional level (5 years or more).

Skills

BigQuery
Databricks
Azure
Snowflake
GCP billing
SQL
Looker Studio
Grafana
Python

Experience Level

7 years or more of equivalent education and experience
4 years or more of cloud FinOps, cloud billing analysis, or data platform cost management
4 years or more of GCP billing proficiency
4 years or more of SQL proficiency
3 years or more of BI tooling proficiency
3 years or more of chargeback and showback model design and presentation
3 years or more of Japanese language proficiency
5 years or more of English language proficiency
2 years or more of Databricks cost management experience
2 years or more of Snowflake cost governance experience
2 years or more of Python scripting experience
2 years or more of enterprise-scale cloud financial management experience

Education Level

Bachelor's Degree (BS) in Computer Science, Finance, Engineering, or a related field

About the Company

The Technology Platforms Division (TPD) drives the growth of Rakuten's ecosystem by delivering innovative, high-quality technology platforms characterized by integrated control and strategic partnerships.
The Cloud Platform Supervisory Department (CPSD) develops and manages Rakuten's state-of-the-art cloud platform, empowering global scalability and accelerating innovation across its diverse business units.
The Data Platform Department (DPD) at Rakuten Group develops and maintains a comprehensive data platform, empowering over 70 Rakuten services with solutions for data ingestion, discovery, governance, analytics, and querying.
We support data-driven decision-making across one of Japan's largest data ecosystems, providing the tools and infrastructure to support key domains such as Data Lakes, Data Warehouses, and Business Intelligence.