AI Data Architect

As a Data/AI Architect, you'll design and build data-driven cloud architectures on AWS — from S3 data lakes and Glue ETL pipelines to data warehouses and RAG-powered AI systems. You'll own the full data stack across a variety of industries and projects: one engagement you're designing a Redshift data warehouse with medallion architecture processing 31M transactions/month, the next you're building a Bedrock Knowledge Base with OpenSearch vector search. Real ownership, real variety.   What You'll Do: Design and build S3 data lakes with multi-zone organization, partitioning strategies, lifecycle policies, and encryption Implement medallion architecture (bronze/silver/gold) for data warehouses on Redshift, Snowflake, or Databricks Build AWS Glue ETL pipelines (Python Shell and Spark) with incremental extraction, Data Catalog management, and optimized Parquet output Design star/snowflake schemas, materialized views, and gold-layer models optimized for BI consumption (QuickSight, PowerBI) Configure data warehouse platforms — Redshift with Zero-ETL from Aurora, Snowflake with Snowpipe, Databricks with Delta Lake and Auto Loader Design RAG systems using Bedrock Knowledge Base with OpenSearch Serverless vector search and Titan Embeddings Architect document AI pipelines using Textract, Comprehend, and Bedrock for entity extraction Design SageMaker ML pipelines for training, Model Registry, and inference Lead data discovery sessions with client stakeholders and present architecture recommendations to technical and business audiences Mentor delivery team members on data architecture patterns and AWS data services Contribute to R&D projects evaluating emerging AWS data and AI capabilities   Required Skills: 5+ years professional IT experience, 2+ years professional AWS experience At least one AWS Professional-level certification (Solutions Architect Professional or Data Engineer Specialty preferred) Python for data pipelines (Glue jobs, Lambda, SageMaker scripts) and PySpark for Glue Spark jobs SQL and NoSQL on AWS — Aurora PostgreSQL, RDS PostgreSQL, DocumentDB, DynamoDB — including schema design and query optimization Data modeling — conceptual, logical, and physical models for AWS data platforms; normalized silver-layer schemas, denormalized star/snowflake gold-layer schemas, data dictionaries Dimensional modeling and medallion architecture (bronze/silver/gold) on Redshift, Snowflake, or Databricks, including materialized views and incremental refresh patterns AWS Glue ETL (Python Shell and Spark), Glue Data Catalog, and crawlers S3 data lake architecture with partitioning, lifecycle policies, and encryptions   Preferred: RAG systems with Bedrock Knowledge Base and OpenSearch Serverless vector search Amazon SageMaker for ML training, Model Registry, and inference AWS HealthLake, FHIR R4 transformation, and HIPAA-compliant data pipelines Document AI with Amazon Textract and Comprehend Amazon Athena, QuickSight, or PowerBI integration Terraform or CloudFormation for data infrastructure as code Step Functions, EventBridge, and Lambda for event-driven pipeline orchestration