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# Senior Delivery Consultant - Data , Professional Services, AWSI HCLS

- **Company:** Amazon.com, Inc.
- **Location:** Dallas, TX, United States
- **Experience:** Expert
- **Salary:** $176,600.0 - $239,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Apache HTTP Server, Clinical Data Repository, Cloud Engineering, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Security, Data Systems, Software Design Patterns, Distributed Computing Environment, Graph Database, Machine Learning, Operational Databases, Software Deployment, Software Engineering, Systems Integration, Fast Healthcare Interoperability Resources, Snowflake, Prompt Engineering, Apache Spark, Data Layers, Data Lakes, Data Lineage, Collibra, AWS Glue, Data Analytics, Apache Kafka, Virtual Agents, Data Delivery, Data Pipelines, GXP, Legacy Systems, Amazon Redshift, Databricks
- **Published:** June 30, 2026
- **Apply:** https://www.amazon.jobs/en/jobs/10461655/senior-delivery-consultant-data-professional-services-awsi-hcls

## About the Role

5+ years of cloud architecture and solution implementation experience

 - Bachelor's degree, or 7+ years of professional or military experience
 - 5+ years of experience in data engineering, data architecture, and/or data platform development, with hands-on implementation of production data pipelines
 - Experience with 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, 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

## Description

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


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