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# Sr. Delivery Consultant - Data , AWS Professional Services

- **Company:** Amazon.com, Inc.
- **Location:** Houston, TX, United States
- **Experience:** Expert
- **Salary:** $153,600.0 - $207,800.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Business Analytics Applications, Big Data, Cloud Computing, Databases, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Security, Data Systems, Elasticsearch, Graph Database, Identity and Access Management, Machine Learning, Cloud Services, Software Engineering, Systems Integration, Apache Spark, Amazon Virtual Private Cloud (VPC), Cloudformation, Amazon Relational Database Service, Data Lakes, Pyspark, Data Analytics, Functional Programming, Data Delivery, Data Pipelines, GXP, Legacy Systems, Databricks
- **Published:** September 2, 2026
- **Apply:** https://www.amazon.jobs/en/jobs/10524339/sr-delivery-consultant-data-aws-professional-services

## About the Role

5+ years of IT platform implementation in a technical and analytical role experience

 - 5+ years of cloud based solution (AWS or equivalent), system, network and operating system experience
 - 5+ years of experience in data analytics implementation, with deep expertise in ETL/ELT pipeline design and deployment (e.g. Informatica, Glue, DBT, PySpark)
 - Hands-on experience implementing AWS and/or third-party data analytics services (e.g. Redshift, Glue, LakeFormation, Databricks, dbt, Spark), Knowledge of data engineering pipelines, cloud solutions, ETL management, databases, visualizations and analytical platforms
 - Experience driving collaborative projects from conception to delivery, or experience with data infrastructures: relational analytic DBMS, Elastic-Search, and Big Data EMR/EC2/Glue/Lambda
 - AWS experience preferred, with proficiency in a wide range of AWS services (e.g., EC2, S3, RDS, Lambda, IAM, VPC, CloudFormation)

## Description

Design and implement production-grade data pipelines, data lakes, lakehouses, and data mesh architectures within enterprise 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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