• Design, build, and maintain robust data ingestion and transformation pipelines that supply the knowledge graph analytics platform. • Profile source data and assess structure, quality, completeness, and integration challenges. • Implement complex transformation, normalization, and preparation logic required to load data into graph solutions. • Support source-to-graph traceability and data lineage to ensure analytic transparency and rapid debugging. • Collaborate with the Lead Data Scientist and engineering peers to align source data processing with graph modeling and entity resolution requirements. • Continuously improve data quality handling and pipeline reliability to ensure high-availability of intelligence insights. • Document ingestion patterns, transformation rules, and technical considerations to support enterprise scaling. • Utilize Jira and GitLab to manage tasks and maintain version control across the development lifecycle.

• End-to-End Pipeline Engineering: Design, build, and deploy scalable ETL/ELT pipelines from diverse source systems into our Snowflake Data Cloud. • Cloud Infrastructure: Manage and optimize data flows within an AWS environment (S3, Lambda, IAM), ensuring high availability, security, and cost-efficiency. • High-Scale Processing: Leverage Databricks and Python (PySpark) to handle complex transformations and high-volume data processing. • Implement the Semantic Layer: Collaborate with the team to define, implement and scale our Semantic Layer (via dbt Semantic Layer, MetricFlow, or similar) to standardize business logic, metrics, and dimensions for all downstream consumers. • Model for Truth: Use dbt to build modular, version-controlled, and tested data models that serve as the definitive foundation for business intelligence. • Data Governance & Quality: Implement automated testing and monitoring to ensure the integrity and reliability of finished data marts.

• Work with AI team members to operationalize data pipelines and ML tasks. • Provide day-to-day support of deploying Python-native ML pipelines and perform data engineering tasks to enable AI/ML capabilities. • Present results to a diverse audience in presentation or report form. • Support architectural leadership, technical support, and advisement services to ensure identity management system technologies are integrated and meeting the appropriate security requirements. • Support leadership who engage with senior level executives at a public facing Federal agency and provide subject matter expertise in security architecture and other key domain areas.

• Maintain and enhance the Snowflake data platform and data pipelines, leveraging AI-assisted development tools to improve efficiency, reliability, and maintainability. • Drive innovation by adopting modern engineering practices, AI-assisted development, and automation to continuously improve the data platform and accelerate delivery. • Design, build, and optimize incremental data integration pipelines with a strong focus on data quality, performance, scalability, and cost efficiency. • Implement and maintain data security controls, support audit and compliance requirements, and monitor platform usage and costs. • Collaborate closely with Engineering, Product, and Operations teams to deliver reliable, scalable data solutions.