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
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Design scalable data pipelines with AWS Glue for ETL orchestration, AWS Lambda for event-driven compute, and Amazon S3 for data lake storage
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Define data architecture patterns aligned to the AWS Well-Architected Framework with a focus on reliability, performance, and cost optimization
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Create technical specifications and architecture diagrams for data platform components
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Lead implementation of Python and PySpark solutions for large-scale processing and transformation
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Architect Amazon OpenSearch and vector database solutions that enable semantic search, retrieval-augmented generation, and AI/ML workloads
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Design data models and pipelines that support advanced analytics, including regression analysis and NLP applications
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Provide architectural guidance on GenAI strategy, advisory work, and operational integration with existing data infrastructure
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Design infrastructure patterns that enable machine learning model training, inference, and deployment workflows
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Advise teams on data preparation and feature engineering approaches for ML/AI use cases
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Establish CI/CD patterns for data pipeline deployment and infrastructure-as-code practices
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Collaborate with engineering teams to ensure data platform components meet operational excellence standards
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Support knowledge transfer and produce technical documentation to ensure sustained delivery
Requirements
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Solid background with 8+ years of experience in data analytics engineering
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Hands-on experience writing production-grade PySpark
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Advanced expertise with AWS Glue, AWS Lambda, and Amazon S3
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Proven track record using Amazon OpenSearch in real-world solutions
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English proficiency at B2 level (Upper-Intermediate) or higher
Nice to have
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Working knowledge of machine learning concepts and workflows
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Familiarity with CI/CD practices for data and platform delivery
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Understanding of vector databases and common usage patterns
We offer
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International projects with top brands
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Work with global teams of highly skilled, diverse peers
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Healthcare benefits
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Employee financial programs
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Paid time off and sick leave
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Upskilling, reskilling and certification courses
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Unlimited access to the LinkedIn Learning library and 22,000+ courses
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Global career opportunities
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Volunteer and community involvement opportunities
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EPAM Employee Groups
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Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn
