Data Solution Architect, AWS

We are building a Data Solution Architect, AWS role to shape cloud-native data pipelines for analytics and GenAI enablement on AWS. You will own architecture across ingestion, transformation, storage, and search/retrieval using AWS services and guide teams on delivery standards. Apply to help design and operationalize this data platform end-to-end.

Responsibilities

• Design scalable data pipelines with AWS Glue for ETL orchestration, AWS Lambda for event-driven compute, and Amazon S3 for data lake storage
• Define data architecture patterns aligned to the AWS Well-Architected Framework with a focus on reliability, performance, and cost optimization
• Create technical specifications and architecture diagrams for data platform components
• Lead implementation of Python and PySpark solutions for large-scale processing and transformation
• Architect Amazon OpenSearch and vector database solutions that enable semantic search, retrieval-augmented generation, and AI/ML workloads
• Design data models and pipelines that support advanced analytics, including regression analysis and NLP applications
• Provide architectural guidance on GenAI strategy, advisory work, and operational integration with existing data infrastructure
• Design infrastructure patterns that enable machine learning model training, inference, and deployment workflows
• Advise teams on data preparation and feature engineering approaches for ML/AI use cases
• Establish CI/CD patterns for data pipeline deployment and infrastructure-as-code practices
• Collaborate with engineering teams to ensure data platform components meet operational excellence standards
• Support knowledge transfer and produce technical documentation to ensure sustained delivery

Requirements

• Solid background with 8+ years of experience in data analytics engineering
• Hands-on experience writing production-grade PySpark
• Advanced expertise with AWS Glue, AWS Lambda, and Amazon S3
• Proven track record using Amazon OpenSearch in real-world solutions
• English proficiency at B2 level (Upper-Intermediate) or higher

Nice to have

• Working knowledge of machine learning concepts and workflows
• Familiarity with CI/CD practices for data and platform delivery
• Understanding of vector databases and common usage patterns