• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver tailored data solutions. • Develop efficient data processing and transformation workflows to support analytics and reporting needs. • Design, build, and maintain data pipelines and automated extract, transform, load (ETL) processes using tools like Python, R, and platform‑specific environments such as Jupyter Notebooks. • Integrate disparate structured and unstructured data from APIs, databases, and cloud storage into unified datasets utilizing ETL patterns, frameworks, query techniques. • Implement processes for data cleaning, transformation, and validation to ensure data accuracy, consistency, and compliance with security and privacy policies. • Develop dashboards, visualizations, and analytical products leveraging QuickSight or other mission‑approved tools to support operational decision‑making. • Provide full lifecycle assistance in deploying, optimizing, maintaining complex code with data processing routines running in development, test, and production • Optimize code through advanced algorithmic concepts to facilitate more efficient