• Serve as the expert for ETL and DB solutions, collaborating with business stakeholders and IT teams to define requirements, gather data, and implement optimized data solutions. • Design, implement, and maintain data systems in Snowflake to ensure data scalability and accessibility. • Implement and manage data lakes and data warehouses, creating pipelines and data models to enable efficient analytics and reporting. • Establish and document strategies for managing data transfer processes, including secure file transfers (SFTP), batch data processing, and real-time streaming. • Build and optimize ETL pipelines for data extraction, transformation, and loading into operational databases or analytical platforms. • Integrate and support data visualization tools such as Power BI, Sisense, Google Looker, Tableau, or similar platforms to enable actionable insights for business stakeholders. • Develop and maintain optimized data models for dashboards and reporting, ensuring compatibility with visualization tools. • Plan, coordinate, and implement database migrations, upgrades, and patches with minimal downtime. • Define and enforce database governance policies, including data integrity, security, and compliance with regulatory requirements. • Analyze and resolve database performance issues by optimizing queries, indexes, and schema designs. • Partner with vendors to evaluate, select, and implement database tools, services, and technologies; stay informed about product roadmaps and industry trends. • Develop disaster recovery and high-availability solutions, including replication, clustering, and failover.

• Design and implement data pipelines (ETL/ELT) using modern tools (e.g., Apache Airflow, DBT, Dataflow); • Integrate data from transactional systems, APIs, and relational and non-relational databases; • Create and maintain optimized data structures in analytical environments (data lakes and data warehouses); • Ensure data governance, data quality, and data cataloging; • Automate routines for data extraction, transformation, and loading; • Support data scientists, analysts, and product squads with reliable, well-modeled data; • Participate in modernization and data migration projects to the cloud; • Monitor and resolve failures in pipelines and other critical data processes.

• Supports ECS/EAD (Enterprise Case Selection/Enterprise Anomaly Detection) data engineering tasks including ingestion scripting, basic transformation logic, and data preparation activities for analytics and operational workloads. • Assists senior engineers in building and maintaining pipelines, validating data accuracy, and troubleshooting low‑complexity ingestion errors. • Performs initial schema mapping, data profiling, and documentation updates. • Works with analysts and data scientists to ensure datasets are complete, accessible, and aligned with ECS/EAD mission and compliance requirements

• Supports ECS/EAD (Enterprise Case Selection/Enterprise Anomaly Detection) data engineering efforts with a focus on automation and repeatable data processing workflows. • Builds automated data validation routines, ETL test harnesses, and monitoring scripts to ensure pipeline reliability, data integrity, and compliance with agency standards. • Implements ingestion and transformation components, integrates with cloud data services, and resolves pipeline defects through automated checks. • Collaborates with senior engineers, analysts, and cloud teams to ensure data flows are accurate, secure, and aligned with ECS/EAD modernization patterns.