• Design and build data pipelines for collecting, processing, and storing healthcare clinical data, and continuously improve them. • Collaborate with data scientists, data integration specialists, analysts, and other stakeholders to understand data requirements and translate them into scalable solutions. • Implement data validation, cleansing, and transformation processes to ensure data accuracy and consistency. • Proactively monitor and troubleshoot pipeline performance, identifying and resolving issues before they become problems. • Ensure data security and compliance with industry regulations, including HIPAA. • Partner with the DevOps team to deploy and manage data pipeline infrastructure in our Azure cloud environment. • Evaluate and adopt emerging tools, frameworks, and techniques that can improve our data platform: bring recommendations, not just questions. • Leverage AI-assisted development tools and modern engineering workflows to accelerate delivery and improve code quality.

• Design and build end-to-end ML pipelines using GCP services (Vertex AI, BigQuery, Dataform) • Develop and productionize tabular ML models (e.g., XGBoost or similar) • Implement robust feature engineering pipelines with point-in-time correctness • Ensure reliable batch scoring workflows and production deployment • Partner with engineering and product stakeholders to translate business needs into ML solutions • Optimize data workflows for performance, scalability, and cost efficiency • Contribute to model evaluation, monitoring, and continuous improvement • Collaborate within a distributed team, ensuring clear communication and delivery alignment

• Define and maintain KSM’s Databricks lakehouse architecture, including ingestion, storage, transformation, modeling, and access patterns • Establish clear, repeatable design standards for Bronze, Silver, and Gold data layers to ensure consistency and reuse • Design architecture that supports structured, semi-structured, and unstructured data across enterprise systems • Design and implement data governance, lineage, and quality frameworks that ensure data is trusted, auditable, and scalable • Enable discoverability, access control, and metadata management using Databricks Unity Catalog and related tooling • Partner with data and analytics teams to define validation standards, reconciliation processes, and ownership models • Partner closely with the Senior Data Engineer to translate architectural standards into production-ready pipelines • Review and validate data pipelines, models, and workflows to ensure alignment with architectural best practices • Support teams by providing guidance, patterns, and examples rather than one-off solutions • Define best practices for Databricks compute and storage optimization, balancing performance and cost • Establish architectural patterns that promote reliability, scalability, and operational simplicity • Collaborate on monitoring, alerting, and operational standards to ensure platform health • Define and support CI/CD standards for data pipelines and lakehouse infrastructure • Partner with engineering teams to implement Infrastructure as Code (IaC) using tools such as Terraform • Ensure consistent, automated deployments across development, test, and production environments • Align data architecture decisions with firm-wide analytics, automation, and AI strategy • Help prioritize architectural investments that deliver near-term value while supporting long-term growth • Evaluate emerging data platform capabilities thoughtfully, focusing on practical adoption rather than experimentation for its own sake.

• Lead end-to-end, non-recurring data and document migrations from legacy case management systems into Filevine. • Partner directly with clients to assess source systems, define migration strategy, and set clear expectations. • Design and build SQL queries and ETL processes to extract, transform, and load structured data. • Develop and document detailed data mappings between source databases and Filevine. • Cleanse, normalize, and validate data to ensure accuracy and integrity before and after migration. • Collaborate with clients to understand their data structures and migration needs. • Troubleshoot and resolve migration-related issues, escalating when necessary. • Conduct post-migration testing and quality assurance. • Maintain detailed documentation of migration processes, issues, and resolutions. • Provide support and training to clients and internal teams on data migration best practices. • Participate in code reviews and continuous improvement initiatives.