Senior Delivery Consultant – Data, ProServe EMEA

AWS EMEA SARL (Switzerland Branch); Genf; Vor Ort; CHF 140.000 - 190.000; Vollzeit

Zusammenfassung

AWS EMEA SARL (Switzerland Branch) is seeking a Delivery Consultant specialized in Data for the Healthcare and Life Sciences practice. You will design and implement modern data platforms, pipelines, and enterprise RAG architectures to enable AI-ready data assets within regulated environments.

You will work hands-on in customer environments with data lineage, compliance overlays (GxP, HIPAA, CDISC), and legacy systems, delivering production-grade data products for AI training and agentic

Qualifikationen

• 5+ years of experience in data engineering, data architecture, and/or data platform development with hands-on production pipelines.
• Bachelor's degree in Computer Science, Engineering, Data Science, or related field, or equivalent experience.
• Proficiency in modern data platform design patterns (data lakes, lakehouses, data mesh, zero-ETL, streaming) using AWS data services.
• Experience architecting ontologies and knowledge graphs in enterprise environments.

Aufgaben

• Design and implement production-grade data pipelines, data lakes, lakehouses, and data mesh architectures within enterprise HCLS environments, integrating with legacy systems and data governance frameworks.
• Build data products serving multiple downstream applications from AI/ML model training to agentic AI systems, ensuring data quality and lineage at scale.
• Operate with high autonomy in fast-moving delivery engagements, making judgments on data modeling, pipeline design, and architecture without constant oversight.
• Navigate complex data access, security, and privacy requirements for pharma/healthcare, including GxP, HIPAA, and regulatory frameworks.
• Architect contextual knowledge layers including ontologies and knowledge graphs leveraging AWS Context and Bedrock Knowledge Bases to empower AI agents.
• Collaborate across teams to secure data access, understand source context, and resolve data quality challenges.
• Deliver iteratively when requirements are ambiguous, translating incomplete business needs into well-architected data solutions.
• Apply AI-DLC methodologies to redesign data workflows for AI-native scale and pace.

Kenntnisse

Data engineering

Data architecture

Data platform development

Ontology & knowledge graphs

Ausbildung

Bachelor's degree in CS/Engineering/Data Science

Tools

SageMaker Lakehouse

SageMaker Unified Studio

Amazon S3 Tables

Amazon Redshift

Apache Iceberg

Spark

Databricks

Snowflake

Kafka

Jobbeschreibung

The Amazon Web Services Professional Services (ProServe) team is seeking a Delivery Consultant specializing in Data to join our Healthcare and Life Sciences (HCLS) practice. You will be at the center of the most consequential shift in enterprise technology: making organizations truly AI-ready. Every agentic AI system, every foundation model grounded in enterprise knowledge, and every GenAI application that moves from prototype to production depends on the data layer beneath it - and that's what you build.

You will design and implement modern data platforms (lake, lakehouse, mesh), architect data pipelines that transform raw, fragmented data estates into governed, AI-ready assets; and design and implement enterprise RAG architectures, vector stores, semantic ontologies, and knowledge graph architectures that allow foundation models and AI agents to reason accurately, access data securely, and execute autonomously within regulated environments. You will work hands-on inside HCLS customer environments with complex data lineage, regulatory overlays (GxP, HIPAA, CDISC), and legacy systems, and ship production-grade data products that serve multiple downstream consumers, from ML model training to agentic orchestration layers.

The AWS Professional Services organization is a global team of experts that help customers realize their desired business outcomes when using AWS services. We work together with customer teams and the AWS Partner Network (APN) to execute enterprise cloud computing and AI transformation initiatives.

Key job responsibilities

• - Design and implement production-grade data pipelines, data lakes, lakehouses, and data mesh architectures within enterprise HCLS environments, integrating with legacy systems and existing data governance frameworks
• - Build data products that serve multiple downstream applications and use cases - from AI/ML model training to agentic AI systems, ensuring data quality, lineage, and reliability at scale
• - Operate with a high degree of autonomy within fast-moving delivery engagements, making judgment calls on data modeling, pipeline design, and architecture without waiting for perfect specifications or constant oversight
• - Navigate complex data access, security, and privacy requirements unique to pharma and healthcare including GxP compliance constraints, HIPAA, and regulatory data governance frameworks
• - Architect contextual knowledge layers, including ontologies and knowledge graphs leveraging AWS Context, Amazon Bedrock Knowledge Bases, and custom ontology extensions to equip AI agents with the vocabulary and guardrails to reason accurately and execute autonomously within regulated environments
• - Collaborate across organizational boundaries to secure data access, understand source system context, and resolve data quality challenges with teams across customer IT, business, and partner organizations
• - Deliver iteratively when requirements are ambiguous, translating incomplete business needs into well-architected data solutions that can evolve as customer understanding matures
• - Apply AI-DLC (AI-accelerated Development Life Cycle) methodologies to data delivery to redesign data workflows to become AI-native for accelerated scale and pace

About the team

Diverse Experiences

Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.

Why AWS

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

Work/Life Balance

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud.

Inclusive Team Culture

Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences.

Mentorship and Career Growth

We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

BASIC QUALIFICATIONS

• - 5+ years of experience in data engineering, data architecture, and/or data platform development, with hands-on implementation of production data pipelines
• - Bachelor's degree in Computer Science, Engineering, Data Science, related field, or equivalent experience
• - Proficiency in modern data platform design patterns, including data lakes, lakehouses, data mesh, and zero-ETL patterns and streaming architectures, using services such as Amazon SageMaker Lakehouse, SageMaker Unified Studio, Amazon S3 Tables, Amazon Redshift, and zero-ETL integrations.
• - Experience with architecting and engineering ontologies and knowledge graphs in enterprise environments

PREFERRED QUALIFICATIONS

• - AWS certifications in Data Analytics or Machine Learning Specialty preferred
• - Experience in the healthcare and life sciences industry, including familiarity with compliance and security frameworks (HIPAA, GxP) and clinical data standards (OMOP, CDISC, FHIR)
• - Hands-on experience with Apache Iceberg, Spark, Databricks, Snowflake, Kafka, or equivalent distributed data processing frameworks
• - Experience designing and implementing knowledge graph architectures, ontology models, or semantic data layers that support AI/ML and agentic AI systems
• - Experience with data governance and cataloging tools (e.g., AWS Glue Data Catalog, Collibra, Alation) and data lineage tracking and designing data access patterns that support identity and least-priviledge access
• - Experience collaborating with customer business teams, IT, and partner organizations to understand data requirements and resolve access challenges and conveying technical concepts to both technical and business audiences.
• - Proficiency in AI-DLC or equivalent AI-accelerated development methodologies - including prompt engineering as a development discipline, mob programming with AI, and experience validating AI-generated data pipeline code for production deployment in regulated environments