Role Description This role provides a unique opportunity to lead the modernization of complex data environments, transforming legacy Oracle BI and ODI workflows into optimized, scalable Snowflake solutions. You will work hands-on to redesign data models, implement ELT transformations, and ensure data quality, performance, and maintainability. The position requires deep technical expertise, analytical thinking, and the ability to untangle legacy structures while designing modern, future-proof architectures. You will collaborate closely with cross-functional teams to validate results and support analytics needs. This role is ideal for someone who thrives in complex migration projects, enjoys problem-solving at scale, and takes pride in delivering clean, efficient, and high-performing data systems. The position is fully remote and designed for self-driven professionals who can balance autonomy with collaborative teamwork. - Analyze, interpret, and reverse-engineer existing Oracle Data Integrator (ODI) workflows and legacy data transformation logic - Document and map current data flows, dependencies, and structures to guide modernization - Design Snowflake-optimized data models and ELT patterns for scalable and high-performance solutions - Write efficient SQL queries and implement ELT transformations within Snowflake - Restructure legacy models into maintainable, normalized architectures, cleansing redundant or outdated artifacts - Build modular, testable transformation pipelines using DBT or similar tools - Manage Snowflake development environments, including roles, access control, and governance - Establish validation, reconciliation, and data quality checks to ensure accuracy during migration Qualifications - 5+ years of experience in data engineering or data consulting roles - Strong hands-on expertise with Oracle Data Integrator (ODI) and legacy transformation logic - Deep experience in Snowflake development, including data modeling and performance optimization - Proficiency with SQL and modern ELT/ETL frameworks, including DBT - Strong understanding of data warehousing, normalization, and best practices for scalable architectures - Experience validating data migrations between legacy and modern platforms - Ability to work independently in complex technical environments while collaborating effectively with QA and review teams - Excellent written and verbal communication skills - Must reside in the United States and be authorized to work without sponsorship now or in the future Benefits - Fully remote work with flexible schedules - Opportunities for professional development and sponsored learning - Hands-on experience modernizing large-scale, high-impact data environments - Collaborative, supportive team culture with emphasis on growth and skill-building - Exposure to cutting-edge data platforms and transformation methodologies - Competitive compensation commensurate with experience

• Crear y mantener pipelines de datos, que permitan la ingesta, el almacenamiento y el procesamiento de los datos. • Desarrollar procesos que faciliten la integración de datos provenientes de distintas fuentes (BBDD, archivos, APIs, …). • Mejorar el rendimiento de los pipelines de datos, asegurando una carga y transformación rápida y confiable. • Crear modelos de datos lógicos y físicos (relacionales y dimensionales). • Automatizar los procesos de ingesta y transformación. • Implementar procesos de calidad que garanticen la fiabilidad de la información procesada. • Desarrollar procesos alineados con las buenas prácticas de CI/CD. • Elaborar la documentación técnica correspondiente.

• Definición e implementación de buenas prácticas sobre una arquitectura Modern Data Stack, asegurando escalabilidad, mantenibilidad y calidad de los datos. • Migración y modernización de procesos ETL/ELT existentes (SQL, scripting, herramientas legacy) hacia plataformas analíticas modernas y frameworks de transformación basados en SQL. • Análisis, control y optimización de costes en plataformas de data warehouse en la nube, identificando oportunidades de mejora en rendimiento y eficiencia. • Refactorización y estandarización de pipelines y modelos de datos, corrigiendo procesos mal diseñados o subóptimos y alineándolos con buenas prácticas de la Modern Data Stack. • Documentación técnica y elaboración de guías de migración, facilitando la adopción de estándares, el traspaso de conocimiento y la autonomía de los equipos.

• Design and evolve the end-to-end data platform architecture, from ingestion to the analytical layer, applying independent judgement on zoning patterns, latency, and scalability • Lead the data integration strategy: evaluate and onboard new sources, manage schema drift, and implement source-side transformations • Design and govern orchestration pipelines, ensuring reliability, idempotence, observability, and scalability • Establish the architecture of the analytical storage layer: object design, roles, access policies (RBAC), and cost and performance optimization • Collaborate with Engineering, Analytics, and Business teams to ensure the architecture meets current and future requirements • Evaluate new technologies and propose evolutions of the data stack • Document architecture decisions (ADRs) and act as the technical reference for the data team • Provide expertise in DataOps and CI/CD applied to Infrastructure as Code (IaC) on data platforms