• Architect, manage, and scale our enterprise Kubernetes infrastructure (EKS/GKE) to ensure high availability and performance across a large fleet of clusters. • Oversee automated continuous platform optimization, shifting away from manual tuning and human toil to scalable, automated resource management systems. • Implement and manage intelligent resource automation platforms to automate workload sizing, placement, and Karpenter configurations by default. • Collaborate cross-functionally with Engineering, DevOps, Platform Engineering, FinOps, and Finance teams to drive sustainable cost avoidance and alignment, moving beyond retroactive cost savings. • Design, implement, and maintain robust CI/CD pipelines, Infrastructure as Code (IaC), and deep observability tools for the Kubernetes ecosystem. • Mentor engineers and lead architectural initiatives to improve platform resilience, security, and operational excellence.

• Act as a trusted technical advisor to customers and solve complex email flows, migration, platform-related security, user management, data collaboration, device management, systems integration and much more. • Create and deliver best practices recommendations, tutorials, blog articles, sample code, and technical presentations adapting to different levels of key business and technical stakeholders. • Support our sales team as the technical expert on customer-facing calls • Communicate effectively via video conferencing for meetings, technical reviews, and onsite delivery activities.

• Design, develop, and maintain robust data pipelines and ETL processes that enable seamless integration, cleansing, harmonization, and transformations in large-scale data processing systems and data lakes/data warehouses using GCP native services. • Implement performance tuning (partitioning, broadcast joins, caching, shuffle optimization). • Integrate data from multiple sources (databases, APIs, cloud storage, potentially streaming). • Migrate legacy data stores and ETL pipelines to modernized, cloud-native architectures on GCP. • Develop scalable batch and real-time streaming data pipelines using Python, SQL, and Apache Beam. • Implement data governance, data quality checks, and secure data access controls in alignment with strict federal security requirements. • Collaborate with data scientists, cloud infrastructure engineers, and mission stakeholders to optimize data models and query performance. • Design and implement CI/CD pipelines to streamline the build, test, and deployment processes.

• Design, deploy, and manage GCP foundational components, including BigQuery, Secret Manager, Vertex AI, VPCs, Cloud Storage, and/or Cloud IAM. • Execute large scale data migrations to GCP using industry best practices and migration tools (e.g., Migrate for Compute Engine). • Implement secure cloud architectures that comply with federal cloud computing security requirements and Zero Trust principles. • Monitor cloud infrastructure performance, troubleshoot migration issues, and optimize costs.