• 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.

• Desarrollar procesos ETL para réplica, extracción y tratamiento de información desde bases de datos SQL Server. • Programar flujos de transformación y carga hacia Data Warehouse. • Analizar requerimientos de negocio y diseñar especificaciones técnicas. • Desarrollar scripts y procesos bajo herramientas Microsoft. • Implementar modelos dimensionales en esquema Estrella o Copo de Nieve. • Elaborar documentación y manuales técnicos. • Trabajar bajo metodología Scrum. • Desarrollar bajo las políticas y estándares establecidos por BAC Regional.

• Establish and maintain technical excellence within the data engineering team • Design and implement robust data solutions aligning with business objectives • Drive data-driven initiatives and ensure successful delivery • Lead by example, combining strong technical execution with mentorship • Engage with stakeholders to understand data needs and technical constraints • Implement with a DataOps mindset, building reliable and efficient data pipelines • Mentor and develop data engineers, contributing to knowledge sharing and thought leadership