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Mid-level metro Data Architect with broad AWS/AI and governance requirements increases candidate competition.
Role requires deep enterprise data architecture, cloud data platforms and finance/regulatory knowledge, reducing cross-industry portability.
Explicit 6+ years, 3+ cloud leadership, and many mandatory platform and governance skills make screening strict.
Own end-to-end data sourcing, modeling, governance, and delivery to enable analytics, reporting, and agentic AI workloads.
Design and implement performant, resilient, and cost-effective data architectures and pipelines, including AI-ready datasets and metadata layers on AWS cloud.
Collaborate with data engineering, AI platform, governance, and business teams to define data standards, policies, and integration ensuring compliance, quality, and scalability for enterprise AI and analytics solutions.
Bachelor's degree in Computer Science, Engineering, Information Systems, or related field (or equivalent experience).
Minimum 6+ years in data engineering, data architecture, or analytics platforms with at least 3+ years in cloud data platforms and enterprise data leadership roles.
Strong experience with cloud data platforms (AWS including S3, Redshift, Glue), data modeling (star schema, 3NF), ETL/ELT pipelines, and AI data preparation.
Work Experience Required: Minimum 6+ years total, 3+ years specifically in cloud data platforms and leadership. Notice period: Not explicitly mentioned in the JD.
Experienced leader adept at bridging data architecture with AI/ML initiatives, especially agentic AI and AI-ready data products on AWS.
Demonstrates deep knowledge of enterprise data governance, security (IAM, data lineage, PII handling), and AWS infrastructure as relevant to AI workloads.
Proficient in designing scalable data models and pipelines for both batch and streaming data to support analytics and AI use cases in complex domains such as financial services.