• Assist in building and maintaining data pipelines for ingesting, transforming, and validating clinical, biological, and real-world data • Support integration of data from multiple sources (e.g., clinical data, analytics outputs, external datasets) • Help develop and optimize ETL/ELT workflows to ensure data quality and reliability • Collaborate with data science and bioinformatics teams to support analytics and machine learning workflows • Contribute to data modeling, documentation, and best practices for data infrastructure • Participate in code reviews, testing, and performance improvements • Participate in Quality Reviews and Troubleshooting • Communicate progress and findings to cross-functional teams

• Design, develop, test, and deploy features across distributed data platforms. • Maintain and enhance data pipelines running on big data and cloud data technologies. • Perform security-related engineering work, including fixing code vulnerabilities, updating secrets and identities, and ensuring secure endpoints. • Implement governance and compliance updates, including data partitioning, access controls, and physical access restrictions. • Write, modify, debug, and troubleshoot production code and services. • Follow standard engineering lifecycle practices, including CI/CD, monitoring, and deployment workflows. • Execute tasks independently with minimal oversight and deliver high-priority items on aggressive timelines. • Collaborate with engineers to ramp up quickly and take ownership of execution work. • Operate within secure environments and follow all access, security, and compliance requirements.

• Lead the continued transition of legacy SAS-based ETL processes to SQL Server, completing remaining migrations and validating results through parallel processing and data reconciliation. • Translate undocumented or minimally documented legacy ETL logic into maintainable, fault tolerant SQL Server and SSIS workflows. • Improve and standardize incremental data processing patterns, reducing reliance on full data refreshes and destructive reload processes. • Own the reliability and performance of ETL pipelines by identifying and resolving bottlenecks, particularly in high-volume and performance-sensitive workflows. • Investigate and correct data flow issues that prevent records from consistently reaching downstream systems across environments. • Support production data operations by partnering with product, engineering, and support teams to triage and resolve data-related issues and support tickets. • Participate in regular operational check-ins and serve as a primary escalation point for ETL and data pipeline concerns. • Document ETL logic, dependencies, and operational processes to reduce institutional knowledge risk and improve long-term maintainability. • Introduce improved logging, monitoring, automation, and repeatability across data integration workflows. • Collaborate with engineering peers and domain experts to establish clearer ownership and standards for ETL and data pipeline practices.

• Design and implement end-to-end data solutions on the Azure platform, including data ingestion, data processing, data storage, and data visualization. • Develop and maintain data pipelines using Azure Data Factory, Azure Databricks, Azure Data Lake Storage, and other relevant tools and technologies. • Collaborate with data architects and data scientists to understand data requirements and design scalable and optimized data models and schemas. • Implement data integration solutions to extract, transform, and load (ETL) data from various sources into Azure data platforms. • Ensure the reliability, availability, and performance of data solutions by monitoring and optimizing data pipelines and storage systems. • Troubleshoot and resolve data-related issues, including data quality, performance, and security concerns. • Collaborate with cross-functional teams to gather business requirements and translate them into technical solutions. • Stay updated with the latest trends and advancements in Azure data technologies and provide recommendations for adopting new tools and techniques. • Perform data profiling, data validation, and data cleansing activities to ensure data accuracy and consistency. • Document technical specifications, data flows, and processes for reference and knowledge sharing.