• Be a data champion and seek to empower others to leverage the data to its full potential • Architect and own our data platform strategy, with a deep understanding of our complex data ecosystem • Lead the design and implementation of scalable data infrastructure that powers both internal and external products • Partner with Product to translate business requirements for data across the company into a technical roadmap and architecture for the platform • Create and maintain data engineering best practices and mentor teams in building robust, observable pipelines • Assemble large, complex data sets that meet functional / non-functional business requirements • Build the infrastructure required for optimal extraction, transformation, and loading of data from various data sources. • Work with Executive, Clinical, Data, and Design teams, to assist with data-related technical issues and support their data infrastructure needs • Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader

• Integración y modelamiento de datos • Diseñar e implementar pipelines de ingesta y transformación de nuevas fuentes de datos (batch y/o near real-time). • Integrar fuentes internas y externas al modelo de Customer 360, asegurando consistencia con los contextos definidos. • Modelar y estructurar datos para su uso en APIs y analytics . • Construcción y mantenimiento de subproductos analíticos basados en contextos (ej: segmentaciones, features, modelos). • Habilitar datasets listos para consumo por equipos de negocio, analytics y data science. • Colaborar en la definición de nuevos casos de uso basados en datos de cliente. • Implementar controles de calidad, validaciones y monitoreo de pipelines. • Asegurar trazabilidad, documentación y buenas prácticas de data governance. • Optimizar pipelines para performance, costo y escalabilidad. • Trabajar de forma cercana con equipos de negocio, analytics, backend y producto. • Participar en el diseño de nuevos contextos y definición de requerimientos de datos. • Contribuir a la mejora continua de la arquitectura y estándares del equipo.

• Collaborate with a global team of data engineers, fostering coordination across time zones and driving operational excellence • Architect and oversee large-scale data ingestion and transformation pipelines using Airflow, DBT, Python, Kafka streaming, and Snowflake • Design and implement real-time and batch ingestion frameworks integrating data from various business systems and APIs • Manage data integration and transformation from enterprise systems such as Salesforce (SFDC), NetSuite, Workday, and other core business applications • Drive data quality, observability and reliability across pipelines through monitoring, alerting, and proactive issue resolution • Collaborate with data science and product teams to support AI assistance bot integration, AI/ML workflows, and product telemetry analytics • Ensure engineering excellence by aligning data practices with DevOps principles, CI/CD automation, and robust data governance standards • Oversee multiple projects simultaneously, managing priorities, and delivery timelines in a dynamic, high-velocity environment • Communicate effectively with business and technical stakeholders to align data engineering initiatives with strategic organizational goals

• Develop and maintain data pipelines required for the extraction, transformation, cleaning, pre-processing, aggregation, and loading of data from a wide variety of data sources using Python, SQL, DBT, and other data technologies • Design, implement, test, and maintain data pipelines/ new features based on stakeholders’ requirements • Develop/maintain scalable, available, quality-assured analytical building blocks/datasets by close coordination with data analysts • Optimize/ maintain workflows/ scripts on present data warehouses and present ETL • Design / develop/maintain components of data processing frameworks • Build and maintain data quality and durability tracking mechanisms to provide visibility into and address inevitable disruptions in data ingestion, processing, and storage • Translate deeply technical designs into business-appropriate representations as well as analyze business needs and requirements ensuring the implementation of data services directly correlates to the strategy and growth of the business • Focus on automation use cases, CI/CD approaches, and self-service modules relevant to data domains • Address questions and concerns from downstream data consumers through appropriate channels • Create data tools for analytics and BI teams that assist them in building and optimizing our product into an innovative industry leader • Stay up to date with data engineering best practices, and patterns, evaluate and analyse new technologies, capabilities, and open-source software in the context of our data strategy to ensure we are adapting, extending, or replacing our core technologies to stay ahead of the industry • Contribute to Analytics engineering process