• Be the bridge between ZAP and Zendesk's customer-facing analytics application team • Drive joint architectural alignment on the data assets that power customer-facing reporting • Set the design patterns for ingestion, modelling, transformation, governance, and consumption • Stay hands-on. Build and review the highest-leverage pipelines and curated datasets yourself • Lead ZAP's transition into an AI-first operating model • Establish engineering best practice for ZAP and influence beyond it • Mentor staff and senior engineers across data engineering and data science

• Collect, manage, and transform large-scale raw data from multiple sources into structured, reliable, and usable formats that meet business and analytical requirements. • Design, develop, and maintain data architectures, pipelines, and applications using appropriate tools and frameworks to support scalable data processing and product development. • Build and maintain robust extract, transform, and load (ETL) processes that ensure data accuracy, efficiency, and availability across systems. • Perform root cause analysis to identify data issues, system inefficiencies, or process gaps, and implement improvements that enhance data quality and operational performance. • Work in an agile Scrum environment by contributing to sprint planning, development tasks, testing, and sprint reviews to deliver reliable and scalable data solutions

• Consultor Ingeniero de Datos Azure • Dataricks • SQL • Transformación de Datos

• Develop and maintain scalable, efficient data pipelines (ETL/ELT); • Work on data ingestion, transformation, and provisioning in GCP environments; • Build solutions using Databricks (Spark/PySpark); • Design and evolve data architectures (Data Lake, Data Warehouse, Lakehouse); • Integrate multiple data sources (APIs, databases, streaming); • Ensure data quality, governance, and reliability; • Monitor performance and optimize cloud costs; • Collaborate with BI, Analytics, and Data Science teams.