We are seeking a Staff Consultant to serve as a long-term, full-time resident Data Architect supporting our customer's data foundation. This role focuses on enabling and operating Amazon SageMaker Unified Studio, building and optimizing serverless data pipelines, managing modern data lake storage formats, and integrating agentic AI services to support data rendering and analytics. The position spans data platform engineering, self-service analytics enablement, and BI delivery through Quick Suite, all aligned with AWS cloud-native best practices. Responsibilities Configure, manage, and support Amazon SageMaker Unified Studio (SMUS), including data catalog, blueprints, and serverless compute capabilities Enable self-service data discovery, exploration, and analysis for business and technical users through SMUS Design and maintain SMUS blueprints and templates for repeatable, governed data workflows, while managing IAM domains, access policies, and governance configurations Architect and maintain the customer's data foundation on AWS, ensuring scalability, governance, and performance Design and optimize data lake architectures using Amazon S3, including modern storage formats (Parquet, Iceberg, Delta), and implement data cataloging, partitioning, and lifecycle management strategies for large-scale environments Integrate Amazon OpenSearch Service for search, analytics, and data exploration use cases Build and optimize serverless data pipelines using AWS Glue and AWS Lambda, including ETL/ELT jobs for data ingestion, transformation, and delivery Ensure pipeline reliability, monitoring, and error handling using AWS-native tooling such as CloudWatch, Glue job metrics, and S3 analytics Support the integration and delivery of Quick Suite (QuickSight / QuickSight Q) for analytics, dashboards, and self-service reporting Integrate agentic AI services to support intelligent data rendering, automated insights, and data-driven decision-making, collaborating with AI/ML teams to connect agentic workflows with the data foundation Apply AWS Well-Architected Framework principles with emphasis on security, reliability, performance efficiency, and cost optimization Document data architectures, SMUS configurations, pipeline designs, and operational runbooks, and conduct regular knowledge transfer sessions to build internal capability Requirements 7+ years of experience in data architecture and engineering roles, preferably within AWS cloud environments Expertise in Amazon SageMaker Unified Studio, including catalog, blueprints, and serverless compute Proficiency in AWS Glue, AWS Lambda, and serverless compute for data workflows Background in Data Lake architectures and Amazon S3 with modern storage formats (Parquet, Iceberg, Delta) Skills in Data Foundation Architecture, covering catalog, governance, and self-service enablement Familiarity with Amazon OpenSearch Service for search, analytics, and data exploration Proficient communication skills in English (B2 level or higher) Nice to have Knowledge of Quick Suite / Amazon QuickSight Understanding of Agentic AI Services (e.g., Bedrock Agents, AgentCore) Competency in Amazon Lake Formation Skills in Infrastructure as Code (CDK, Terraform) Strong knowledge of Python
