• 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

• Design, build, and maintain ETL/ELT pipelines and integrations across legacy and cloud systems. • Model, store, and transform data to support analytics, reporting, and downstream applications. • Build API-based and file-based integrations across enterprise platforms. • Ensure compliance with federal security and data governance frameworks (HIPAA, NIST, RMF, CMMC). • Implement monitoring, logging, and data quality checks for reliable, scalable pipelines. • Partner with analysts and data science teams to deliver trusted, AI-ready datasets. • Support data migration and integration initiatives across enterprise systems. • Maintain schema management, data lineage, and documentation to support audit and traceability. • Troubleshoot ingestion, pipeline, and data quality issues.

• Lead scalable data engineering efforts that empower cross-functional teams with reliable, timely, and actionable data • Build and optimize scalable, efficient ETL and data lake processes that proactively catch issues before they impact the business • Own the ingestion, modeling, and transformation of structured and unstructured data to support reporting and analysis across all business units • Partner closely with BI and Analytics to deliver clean, query-ready datasets • Maintain and enhance database monitoring, anomaly detection, and quality assurance workflows • Drive adoption and advancement of Looker dashboards by ensuring robust and scalable backend data support • Contribute to the future of Launch Potato’s data team by mentoring peers and shaping a high-performance, quality-first engineering culture

• Lead scalable data engineering efforts that empower cross-functional teams with reliable, timely, and actionable data, ensuring Launch Potato’s analytics and business intelligence infrastructure fuels strategic growth. • Build and optimize scalable, efficient ETL and data lake processes that proactively catch issues before they impact the business • Own the ingestion, modeling, and transformation of structured and unstructured data to support reporting and analysis across all business units • Partner closely with BI and Analytics to deliver clean, query-ready datasets that improve user acquisition, engagement, and revenue growth • Maintain and enhance database monitoring, anomaly detection, and quality assurance workflows • Serve as the internal point of contact for reporting infrastructure—delivering ad hoc data analyses and driving consistent data integrity • Drive adoption and advancement of Looker dashboards by ensuring robust and scalable backend data support • Contribute to the future of Launch Potato’s data team by mentoring peers and shaping a high-performance, quality-first engineering culture