• Design, build, and optimize distributed query solutions using Starburst Enterprise Platform (SEP) and Trino • Enable secure and governed access to data across multiple sources including cloud platforms, data lakes, and enterprise data warehouses • Implement and enhance data federation, performance tuning, and query optimization strategies • Support identity and access management (IAM) and data governance requirements in a federal environment • Develop and maintain backend services, APIs, and platform components supporting the data ecosystem • Contribute to CI/CD pipelines, observability, and platform reliability initiatives • Collaborate with product, UX, security, and infrastructure teams to deliver scalable and secure data solutions • Troubleshoot performance issues in distributed and cloud-native environments

• Build and maintain ETL/ELT pipelines using Python and SQL • Develop ingestion workflows with AWS Firehose, S3, and related services • Create and optimize dbt models, tests, and incremental logic • Tune Snowflake queries and warehouse usage for cost and performance • Operate and improve Airflow DAGs for reliable execution and monitoring • Maintain high data quality, data integrity, and pipeline SLA commitments • Bring clarity to ambiguous requirements and propose practical solutions • Build feature pipelines to support ML workflows • Support model deployment, monitoring, and automated retraining • Add data validation and quality checks across ML and analytics pipelines.

• Build and maintain ETL/ELT pipelines using Python and SQL • Develop ingestion workflows with AWS Firehose , S3, and related services • Create and optimize dbt models, tests, and incremental logic • Tune Snowflake queries and warehouse usage for cost and performance • Operate and improve Airflow DAGs for reliable execution and monitoring • Maintain high data quality, data integrity, and pipeline SLA commitments • Bring clarity to ambiguous requirements and propose practical solutions • Lead data engineering best practices and influence architectural decisions • Drive projects independently, from definition to delivery • Collaborate with engineering, analytics, and ML teams to support shared goals • Build feature pipelines to support ML workflows • Support model deployment, monitoring, and automated retraining • Add data validation and quality checks across ML and analytics pipelines

• Design, build, and maintain data pipelines using Snowflake, Airflow, and DBT • Lead architectural discussions around the modern data stack • Develop scalable ETL and ELT processes using Python and SQL • Build and enforce data quality frameworks across the platform • Integrate internal and external systems via REST APIs • Partner with data science and product teams to prototype and launch data products • Contribute to code reviews and mentor teammates • Leverage AI coding tools while maintaining high quality and security standards