Job description

FinOps Data Engineer – AI Cost & Usage Analytics Gorilla Logic is looking for a FinOps Data Engineer – AI Cost & Usage Analytics to help build scalable data solutions that provide visibility into AI usage, consumption, and costs across multiple platforms and providers. In this role, you will work at the intersection of data engineering, cloud financial management, and AI operations, building reliable pipelines that ingest usage and billing data from platforms such as GitHub Copilot, Anthropic Claude, Google Gemini, OpenAI, Azure OpenAI, and other emerging AI technologies. You will be responsible for transforming disparate usage and cost data into a unified data model in Google BigQuery and enabling trusted, self-service analytics through Power BI. You will collaborate closely with FinOps, Finance, Engineering, and other stakeholders to turn complex usage data into actionable insights that support cost allocation, forecasting, optimization, and informed decision-making. Responsibilities Design, build, and maintain scalable data pipelines that ingest AI usage, billing, and consumption data from multiple providers and platforms. Develop API integrations and scheduled data ingestion processes using Python, Cloud Functions, Cloud Composer/Airflow, or similar technologies. Build reliable ingestion processes with appropriate error handling, retry mechanisms, monitoring, and data-quality validation. Monitor vendor APIs and data schemas and adapt integrations as providers evolve. Design and maintain a unified BigQuery data model that normalizes usage and billing data across AI providers, including token-based, consumption-based, and seat-based pricing models. Build dimensional and analytical data models that support cost allocation and reporting across teams, business units, projects, providers, models, and cost centers. Develop transformation workflows using SQL and tools such as dbt or Dataform to cleanse, deduplicate, normalize, and enrich data. Integrate usage and cost data with organizational hierarchies, cost centers, and financial structures to support chargeback and showback reporting. Optimize BigQuery datasets through effective partitioning, clustering, incremental processing, and query optimization. Implement historical snapshots and slowly changing dimensions to support accurate trend and point-in-time analysis. Apply appropriate data governance, security, access control, auditability, and PII-handling practices. Build and maintain Power BI dashboards and reporting solutions that provide visibility into AI spend, consumption trends, cost allocation, budgets, forecasts, and utilization. Optimize BigQuery-to-Power BI connectivity and data models to balance performance, scalability, cost, and data freshness. Develop executive-ready dashboards while enabling detailed drill-down analysis for Engineering, Finance, FinOps, and other stakeholders. Implement appropriate row-level security and access controls within Power BI. Partner with FinOps and business stakeholders to define meaningful cost and usage metrics, KPIs, and anomaly-detection logic. Support budgeting, forecasting, cost allocation, and AI spend optimization initiatives with accurate and trusted data. Maintain clear documentation for data lineage, pipeline architecture, transformation logic, and reporting definitions. Identify opportunities to automate manual reporting processes and continuously improve the reliability, scalability, and cost efficiency of the data platform. Collaborate with cross-functional teams in an Agile environment, contributing to technical decisions, planning, and continuous improvement. Technical Requirements 3+ years of professional experience in Data Engineering, Analytics Engineering, Software Engineering, or a related role. Strong proficiency in SQL and experience designing and maintaining analytical data models. Hands-on experience with Google BigQuery or comparable cloud data warehouses such as Snowflake or Amazon Redshift. Strong experience building data pipelines using Python. Experience with workflow orchestration technologies such as Apache Airflow, Google Cloud Composer, Dagster, or similar tools. Hands-on experience integrating with REST APIs, including authentication, OAuth, pagination, rate limiting, and error handling. Experience designing reliable data ingestion, transformation, validation, and monitoring processes. Hands-on experience with Power BI, including semantic/data modeling, DAX, dashboard development, and report optimization. Understanding of cloud billing and cost data structures across platforms such as GCP, AWS, or Azure. Experience working with Git and version-controlled development workflows. Experience implementing CI/CD practices for data pipelines and transformation code. Understanding of dimensional modeling, incremental processing, historical data management, and data-quality practices. Strong problem-solving skills and the ability to work with complex or ambiguous data requirements. Strong communication and collaboration skills, with the ability to work effectively with both technical and business stakeholders. Nice to Have Knowledge of FinOps principles, practices, and frameworks; FinOps Foundation certification is a plus. Experience integrating with AI/LLM usage or billing APIs, such as OpenAI, Anthropic Claude, Google Vertex AI/Gemini, Azure OpenAI, or GitHub Copilot. Experience with dbt or Dataform for data transformation, testing, and documentation. Experience with enterprise cost allocation, chargeback, or showback models. Experience with Terraform or other Infrastructure as Code (IaC) technologies. Familiarity with data governance, security, and compliance practices in enterprise environments. Experience building cost analytics, anomaly detection, forecasting, or cloud optimization solutions.

Requirements

Must-haves

FinOps Data Engineer – AI Cost & Usage Analytics
Gorilla Logic is looking for a FinOps Data Engineer – AI Cost & Usage Analytics to help build scalable data solutions that provide visibility into AI usage, consumption, and costs across multiple platforms and providers.
Design, build, and maintain scalable data pipelines that ingest AI usage, billing, and consumption data from multiple providers and platforms.
Develop API integrations and scheduled data ingestion processes using Python, Cloud Functions, Cloud Composer/Airflow, or similar technologies.
Build reliable ingestion processes with appropriate error handling, retry mechanisms, monitoring, and data-quality validation.
Monitor vendor APIs and data schemas and adapt integrations as providers evolve.
Design and maintain a unified BigQuery data model that normalizes usage and billing data across AI providers, including token-based, consumption-based, and seat-based pricing models.
Build dimensional and analytical data models that support cost allocation and reporting across teams, business units, projects, providers, models, and cost centers.
Develop transformation workflows using SQL and tools such as dbt or Dataform to cleanse, deduplicate, normalize, and enrich data.
Integrate usage and cost data with organizational hierarchies, cost centers, and financial structures to support chargeback and showback reporting.
Optimize BigQuery datasets through effective partitioning, clustering, incremental processing, and query optimization.
Implement historical snapshots and slowly changing dimensions to support accurate trend and point-in-time analysis.
Apply appropriate data governance, security, access control, auditability, and PII-handling practices.
Build and maintain Power BI dashboards and reporting solutions that provide visibility into AI spend, consumption trends, cost allocation, budgets, forecasts, and utilization.
Optimize BigQuery-to-Power BI connectivity and data models to balance performance, scalability, cost, and data freshness.
Develop executive-ready dashboards while enabling detailed drill-down analysis for Engineering, Finance, FinOps, and other stakeholders.
Implement appropriate row-level security and access controls within Power BI.

Nice-to-haves

Knowledge of FinOps principles, practices, and frameworks; FinOps Foundation certification is a plus.
Experience integrating with AI/LLM usage or billing APIs, such as OpenAI, Anthropic Claude, Google Vertex AI/Gemini, Azure OpenAI, or GitHub Copilot.
Experience with dbt or Dataform for data transformation, testing, and documentation.
Experience with enterprise cost allocation, chargeback, or showback models.
Experience with Terraform or other Infrastructure as Code (IaC) technologies.
Familiarity with data governance, security, and compliance practices in enterprise environments.
Experience building cost analytics, anomaly detection, forecasting, or cloud optimization solutions.

Application prep

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