• 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.

• Design AWS data engineering components including Lambda, S3, and database integrations. • Build and operate data pipelines that integrate AWS systems with Snowflake. • Implement Snowflake features including Snowflake AI components (Cortex, Cortex Code, AI functions). • Own design and implementation of data marts, data warehouses, and data lakes. • Develop data models and define process flows for end-to-end data integration. • Produce initial architecture drafts independently for review by the architecture team. • Collaborate with architects and stakeholders to refine designs and ensure alignment with project goals. • Focus on senior-level delivery of data engineering solutions and handoff to operational teams.