Director, Data Observability & FinOps Leader (US Citizen or Green Card Only)

Director, Data Observability & FinOps Leader

Location: Houston, Dallas, or Austin, TX, US Citizen or Green Card Only

Work Model: Hybrid – onsite presence expected, with Monday–Wednesday being the primary collaboration days

Position Overview

We are seeking a senior-level Data Observability & FinOps Leader to help shape the strategy, governance, and optimization of a large-scale GCP Data & AI ecosystem.

This role sits at the intersection of Data Architecture, Data Engineering, Cloud, AI/ML, and FinOps and will focus on two critical objectives: ensuring enterprise data platforms are reliable, observable, and well governed, while also improving visibility and accountability around the cost of data and AI workloads.

This is a highly visible technical leadership position. The ideal candidate has a strong hands-on technical foundation but has progressed into a role focused on architecture, strategy, governance, optimization, and technical guidance. While engineering teams will perform much of the implementation, this leader must have the technical depth to identify issues, recommend solutions, challenge architectural decisions, and drive remediation.

Key Responsibilities

Data Observability & Reliability

• Define and advance an enterprise-wide strategy for Data Observability and reliability across cloud-based data and AI platforms.
• Establish standards for monitoring data quality, pipeline health, lineage, freshness, volume, schema changes, and data contract compliance.
• Develop approaches for proactive anomaly detection and intelligent monitoring across data pipelines and services.
• Establish and track SLAs/SLOs for critical data products and services.
• Improve alerting and root-cause analysis (RCA) processes to reduce data downtime and accelerate issue resolution.
• Drive the evolution from reactive incident management toward proactive data platform governance.
• Develop platform health metrics, operational dashboards, and executive-level reporting.
• Partner with engineering and support organizations to ensure identified reliability issues are prioritized and remediated.

Data FinOps & Platform Cost Optimization

• Establish greater visibility and accountability for costs across enterprise data, analytics, and AI workloads.
• Analyze cloud consumption across queries, compute, storage, pipelines, streaming workloads, data movement, and AI services.
• Develop standards for cost allocation, tagging/labeling, showback/chargeback, and ownership by product, domain, or team.
• Monitor and optimize BigQuery usage, including query efficiency, slot/on-demand consumption, storage strategy, and workload patterns.
• Identify opportunities to reduce Cost Per Query (CPQ) and other workload-level costs.
• Provide technical recommendations involving SQL optimization, partitioning, clustering, materialization, reservation planning, and architecture improvements.
• Partner with Cloud and Finance teams on spend forecasting, budget-to-actual reporting, cost anomalies, and optimization targets.
• Translate technical consumption data into meaningful business metrics such as cost per workload, cost per domain, platform ROI, and cost-to-serve.

AI/ML & Emerging Technology Optimization

• Help establish observability and cost-governance practices across AI/ML and Generative AI workloads.
• Evaluate consumption and performance associated with Vertex AI, LLM APIs, inference workloads, vector search, embeddings, and training data.
• Support intelligent and AI-powered observability capabilities, including anomaly detection and lineage-assisted root-cause analysis.
• Identify opportunities to improve AI pipeline efficiency and prevent unnecessary consumption-based spending.
• Contribute to architecture and governance standards for emerging agentic AI capabilities.

Modern Data Platform Optimization

Provide technical oversight and recommendations across technologies and workloads including:

• Google Cloud Platform (GCP)
• BigQuery
• Cloud Composer
• Vertex AI
• SQL and analytical workloads
• dbt / Dataform
• Kafka / Google Cloud Pub/Sub
• Data pipelines and streaming workloads
• Cloud/serverless storage and data services
• AI/ML platforms and services

Leadership & Governance

• Partner across Data Architecture, Data Engineering, Cloud Platform, AI/ML, Finance, Operations, and Governance organizations.
• Establish operating standards, governance processes, optimization priorities, and escalation paths.
• Provide technical direction to engineering and delivery teams without needing to personally execute every implementation.
• Drive accountability for remediation of reliability, performance, governance, and cost issues.
• Participate in architecture and governance forums and represent Data Architecture when needed.
• Develop executive presentations, recommendations, platform health reporting, and optimization plans for senior leadership.
• Communicate complex technical and financial concepts in clear business terms.

What We're Looking For

• Significant experience in Data Architecture, Data Engineering, Data Platforms, Data Reliability, Data Observability, Cloud FinOps, or related technical leadership roles.
• Strong experience with Google Cloud Platform, particularly BigQuery and enterprise data environments.
• Demonstrated understanding of Data Observability, including data quality, lineage, pipeline monitoring, anomaly detection, schema evolution, freshness, alerting, and SLAs/SLOs.
• Experience identifying and resolving performance and cost issues within modern cloud data platforms.
• Strong understanding of BigQuery/SQL optimization, including partitioning, clustering, workload optimization, and efficient query design.
• Experience with cloud consumption and cost-management concepts, ideally involving data-specific FinOps rather than infrastructure cost management alone.
• Familiarity with AI/ML platform economics, including consumption-based AI services, is highly desirable.
• Ability to evaluate technical architectures, identify opportunities for improvement, and provide actionable recommendations to engineering teams.
• Strong executive communication, presentation, stakeholder-management, and cross-functional leadership skills.

Equal Opportunity Employer