Data Platform Engineering: Build, maintain, and continuously improve a scalable, automated Self-Service Data Platform that enables teams to develop and operate data products efficiently.

Data Product Development: Design, implement, and operate reliable data products for regulatory reporting, operational insights, and advanced analytics.

Data Pipeline Automation: Develop and optimize automated data ingestion, transformation, aggregation, and ETL/ELT workflows across multiple source systems.

AI and Machine Learning Enablement: Provide high-quality data pipelines and infrastructure to support AI model development, deployment, lifecycle management, and integration into operational systems.

API and Data Integration: Develop and maintain APIs and data interfaces that enable secure, reliable, and efficient data exchange between systems and applications.

Data Quality and Governance: Implement data quality controls, validation mechanisms, monitoring, and traceability to ensure data integrity, consistency, and fitness for purpose.

Privacy and Regulatory Compliance: Ensure that data solutions comply with applicable data privacy, information security, and regulatory requirements in the medical technology environment.

Cross-Functional Collaboration: Work closely with data scientists, software engineering teams, enterprise architects, and business stakeholders to identify requirements and deliver reusable data solutions.

Documentation and Data Lineage: Maintain comprehensive technical documentation, data mappings, metadata, and end-to-end data lineage across systems and data products.

• Technology and Automation: Evaluate and introduce technologies, engineering practices, and automation approaches that improve platform scalability, developer productivity, and operational reliability.