• Design and implement a new and well-architected data platform, utilizing market-leading technologies and Platforms (DataBricks, Snowflake, etc.) • Help accelerate our ongoing migration from legacy data systems (MS SQL, SSIS, SSAS, SSRS) to the new platform • Ingest and aggregate data from both internal and external data sources to build our datasets • Help with data-related engineering topics to enable reporting and dashboarding • Improve the productivity of data analysts and enable a higher degree of self-service • Build data pipelines and data-powered products • Work closely with cross-functional tech teams and drive excellence in our engineering, planning, and architecture • Inspire, guide, and teach professionals in the data team and beyond, about valuable trends and best practices in data engineering • Participate in machine learning, data science, and AI initiatives together with other professional team members • Support our amazing culture where we care about our customers and a productive and healthy team atmosphere.

• Design, build, and own scalable data platforms on Google Cloud • Drive end-to-end data solutions, defining best practices, and mentoring team members • Work closely with stakeholders, analysts, and data scientists to deliver reliable, high-performance data pipelines and analytics platforms

• Analysis, Design, implementation, and maintenance pipelines that produce business-critical data reliably and efficiently using cloud technologies. • Develop new ETLs (Extract, Transform, Load), using the current Apache Airflow. • Propose new initiatives to improve performance, scalability, reliability, and overall robustness. • Collect, process, and clean data from different sources using Python & SQL. • Work side by side with the main Architects and Developers to create and ensure best practices and guidelines are being used properly by all projects. • Assess and communicate effectively the effort for required developments. • Discover new data sources to improve new and existing data pipelines. • Be in charge of building and maintaining data pipelines and data models for new and existing projects. • Maintain detailed documentation of your work and changes to support data quality and governance. • Provide feedback and expert points of view as needed to help all data initiatives in the company. • Improve the quality of existing and new data processes (ETL), incorporating statistical process control, and creating alerts when anomalies are received from data sources at every step of the pipeline. • Create benchmark control of execution times for every pipeline, to control and identify potential availability issues.

• Focus on revolutionizing healthcare through AI and generative AI technologies. • Design, implement, and optimize ETL/ELT pipelines for diverse healthcare data sources. • Collaborate with data scientists and AI engineers to create datasets and enhance data readiness for AI/ML applications. • Manage and optimize various databases including Postges, NoSQL, graph databases, and cloud-based databases like Snowflake and Redshift. • Ensure efficient storage, retrieval, and integration of data across different systems. • Work closely with cross-functional teams to understand and address data needs.