AmazonWebServices

Overview In this role, you design and deliver production-grade data platforms for HCLS, enabling AI-ready data environments. You will build and integrate data lakes, lakehouses, and data mesh architectures, ensuring governance and security in regulated settings. You’ll architect knowledge layers and ontologies to support AI agents and foundation models, working hands-on in customer environments. This role emphasizes autonomous delivery, cross-team collaboration, and iterative value realization in complex healthcare data landscapes.

Leistungen / Benefits work-life balance flexible working culture inclusive team culture mentorship and career growth opportunities to influence enterprise AI strategy Verantwortungsbereiche Design and implement production-grade data pipelines, data lakes, lakehouses, and data mesh architectures in enterprise HCLS environments, integrating with legacy systems and governance frameworks Create data products serving multiple downstream applications, ensuring quality, lineage, and reliability at scale Operate autonomously within fast-moving delivery engagements, making design and architecture decisions Navigate data access, security, and privacy requirements in pharma/healthcare, including GxP, HIPAA, and regulatory governance Architect contextual knowledge layers, including ontologies and knowledge graphs, leveraging AWS Context, Bedrock Knowledge Bases, and custom extensions Collaborate

across customer IT, business, and partner teams to secure data access and resolve data quality challenges Deliver iteratively amid ambiguity, turning incomplete business needs into scalable data solutions Apply AI-DLC methodologies to redesign data workflows for AI-native, accelerated delivery Zentrale Anforderungen 5+ years in data engineering, data architecture, or data platform development with production pipelines Bachelor's degree in Computer Science, Engineering, Data Science, or related field, or equivalent experience Proficiency with data platform patterns (lakes, lakehouses, data mesh, zero-ETL, streaming) using SageMaker Lakehouse, SageMaker Unified Studio, S3 Tables, Redshift, and zero-ETL integrations Experience in ontology and knowledge graph design in enterprise contexts collaboration across teams problem-solving and autonomous decision-making ability to communicate complex

concepts to technical and business audiences data platform design: data lakes, lakehouses, data mesh, zero-ETL, streaming advanced data architectures for AI: RAG, knowledge graphs, ontologies AWS data and AI services: SageMaker Lakehouse, SageMaker Unified Studio, AWS Context, Bedrock Knowledge Bases