• Design, develop, and deploy large scale data processing pipelines, both batch and streaming, using technologies such as Dataflow, Apache Beam, Spark, Akka, Pub/Sub. • Expertise with multiple data storage technologies such as Bigtable/HBase, BigQuery, Spanner, CloudSQL/Postgres. • Work with stakeholders to understand business problems, develop use-cases, and translate them into pragmatic and effective technical solutions. • Design and develop appropriate schema for data based on understanding of the domain problem. • Manage data lineage and ensure data security with appropriate tools and methodologies. • Collaborate with data scientists, architects, and other stakeholders to ensure alignment between technical and business strategy. • Continuously monitor, refine and report on the performance of data management systems. • Mentor junior data engineers, reviewing their outputs and directing their professional development.

• 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