Define the enterprise data architecture supporting operational systems, analytical platforms, and AI initiatives.
Establish the event-driven integration patterns, governance and metadata standards, and reusable data products.
Assess legacy systems to understand data structures, data quality, storage mechanisms, and transformation logic.
Work closely with the Systems Architect on enterprise direction, with Solutions Architects on product-level data needs, and with Data Engineers.
Define the target-state data architecture spanning operational stores and the analytical data lake.
Develop conceptual, logical, and physical data models that support both transactional systems and analytics.
Design event-driven integration patterns that feed the EDL, using messaging and an enterprise service bus.
Define reusable data products with clear ownership, documented schemas, lineage, quality expectations, and access policies.
Define data governance, metadata, and cataloging standards.
Ensure the data architecture supports analytics, machine learning, and Generative AI use cases.
Requirements
Minimum of 8 years with BS/BA
Minimum of 6 years with MS/MA
Minimum of 3 years with PhD
Demonstrated experience in enterprise data architecture across operational and analytical systems.
Hands‑on experience with at least one modern data engineering stack.
Strong knowledge of relational and non‑relational databases (e.g., SQL, NoSQL, Hadoop, Spark, Kafka, Kinesis) and data modeling.
Experience designing event‑driven and streaming data integration.
Experience with programming languages (e.g., Java, SQL, Scala, Python).
Experience with schema definition, data governance, metadata management, and data quality practices and software engineering best practices including secure, testable, and maintainable code.
Experience with cloud data platforms, preferably AWS.