• 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.

• Define required tags, allowed values, and tagging standards (dataset/table/column), including inheritance rules. • Map database objects to logical names and plain-language definitions aligned to mission and functional use. • Translate governance requirements into implementable tagging rules and measurable acceptance criteria. • Define policy-enabling tags that support ABAC/ICAM enforcement use cases (e.g., classification, CUI/PII, dissemination/release constraints). • Specify minimum provenance/lineage fields needed to explain source, movement, and transformations for auditability. • Set metadata quality gates for completeness and correctness; manage and document exceptions/deviations with a clear workflow. • Provide rules, templates, and patterns that enable automation (pattern-based tagging, rulesets, validation checks). • Validate export structures for catalog ingestion readiness (required fields, traceability) and support JSON/XML/CSV outputs. • Brief stakeholders, support PoC demonstrations, and capture feedback for scaling beyond the PoC dataset.