Design and maintain the enterprise data architecture, including data models, data flows, integration patterns, and reference architectures across data lakes, warehouses, and operational systems to support current and future needs

Develop and own data modeling standards and ensure database schemas are optimized for performance, scalability, and maintainability

Define and implement data integration strategies and ETL/ELT patterns to ingest, transform, and distribute data from internal and external sources

Collaborate with data, IT and business teams to translate business requirements into robust and reusable data and AI solutions across the organization

Coordinate with the Group Data Team to align on data platform architecture, standards, and governance policies

Monitor and optimize the performance, reliability, and cost‑efficiency of data solutions, driving continuous improvement across the data landscape

Document data assets and workflows to promote transparency, reusability, and cross-functional understanding

Establish and enforce data governance, including data quality rules, master and reference data standards, metadata management, and lineage

Define and implement data security and privacy controls (e.g., access control, encryption, masking) in line with regulatory and compliance requirements.

Provide technical leadership, guidance, and best practices to junior data engineers and analysts on data architecture and standards

5+ years of proven experience in data architecture, data engineering, database administration, or related roles, with hands on delivery of enterprise data solutions

Experience with at least one major cloud platform (e.g., AWS, Azure, GCP) and modern DevOps or DataOps practices is preferred

Proficiency in SQL, Python and experience with ETL/ELT tools and frameworks, as well as data warehousing, datalake design and real-time data streaming

Familiarity with big data and streaming technologies (e.g., Hadoop ecosystem, Spark, Kafka) and NoSQL databases

Familiarity with AI/ML workflows, including data preparation and model integration into production systems

Strong understanding of data governance, data quality, metadata management and data catalog practices

Knowledge of data security, privacy, and compliance concepts, including role‑based access control and protection of sensitive data

Strong communication and stakeholder management skills, with the ability to explain complex data concepts to non‑technical audiences and translate business needs into technical solutions

Problem‑solving mindset and track record of designing scalable, maintainable architectures in complex environments

Knowledge of healthcare data standards (e.g., HL7, FHIR) is highly desirable