• Create and maintain data pipelines for key data and analytics capabilities in the enterprise • Collaborate within an agile, multi-disciplinary team to develop optimal data integration and transformation solutions • Document and analyze data requirements (functional and non-functional) to develop scalable, automated, fault-tolerant data pipeline solutions for business and technology initiatives • Profile data to assess the accuracy and completeness of data sources and work with business partners to mitigate issues • Build and maintain data pipelines using appropriate tools and practices in development, test, and production environments • Design with modularity to leverage reuse of code wherever possible • Create data mappings, programs, routines, and SQL to acquire data from legacy, web, cloud, and purchased package environments into the analytics environment • Use a mix of ELT, ETL, data virtualization, and other methods to optimize the balance of minimal data movement against performance • Maintain metadata management processes and documentation • Monitor data quality to detect emerging issues and consult with the team to create transformation rules to cleanse against defined rules and standards • Participate in code reviews and unit testing to optimize performance and minimize issues.

• Design, build and maintain enterprise data solutions using Microsoft Fabric • Develop scalable data ingestion pipelines from APIs, databases and file-based sources • Build and maintain Lakehouse architectures using the Medallion (Bronze, Silver and Gold) approach • Develop robust data models and semantic layers to support business reporting • Create and maintain Power BI semantic models and dashboards • Implement data quality, validation and reconciliation frameworks • Design secure, governed data solutions including Row-Level Security and access controls • Build and maintain PySpark notebooks for data transformation • Develop SQL and Python-based ETL/ELT processes • Configure and maintain Fabric workspaces, OneLake environments and deployment pipelines • Work closely with solution architects to implement scalable platform designs • Produce technical documentation and engineering standards • Collaborate with UK-based stakeholders and delivery teams • Support future client projects across the TXP Data & AI Practice

• Play a critical role in migrating insurance policy data from administration systems to a modern policy administration platform. • Perform analysis-first role with a data engineering component. • Deep-dive into data, trace and reconcile information across multiple data layers (databases and files). • Build mappings and transformations that move legacy data into the target format. • Resolve data quality issues to deliver validated loads. • Utilize strong, hands-on command of SQL for analysis, tracing, and transforming data. • Use AI tooling to accelerate work processes. • Receive mentorship from an experienced conversion manager. • Work through entire data migration lifecycle, from initial discovery through production cutover. • Support iterative delivery cycles with effective communication.

• Definir la estrategia analítica y el modelo funcional de la solución. • Diseñar y construir el pipeline de ingesta y transformación sobre AWS. • Configurar la seguridad de la infraestructura (IAM). • Diseñar el modelo dimensional (star schema). • Actuar como autoridad técnica frente al cliente.