Database Architect

Asterism IT Solutions•Austin, TX

15h

About The Position

We are seeking an experienced Database Architect to implement an enterprise-wide Data Catalog / Master Data Management (MDM) solution covering both structured and unstructured data. The Database Architect will be responsible for establishing and configuring the semantic layer and data lineage required to support AI tools, analytics, machine learning, and generative AI use cases. The ideal candidate will bridge data engineering, data governance, database architecture, and AI consumption requirements to ensure high-quality, well-documented, secure, and accessible data assets. The candidate will also establish continuous processes to keep the Data Catalog/MDM solution updated as data and systems change while maintaining data quality, consistency, governance, and lineage.

Requirements

7+ years of experience working as an Enterprise Database Architect with experience implementing MDM/Data Catalog solutions.
4+ years of experience as a Data Modeler/DBA working with Oracle and SQL Server RDBMS.
4+ years of expertise with enterprise Data Catalog platforms, such as: Microsoft Purview, Collibra, Alation, or similar enterprise data catalog solutions.
3+ years of experience with AI/ML technologies and semantic/context layer design and integration.
3+ years of experience with cloud technologies and tools in AWS and Azure.
Experience with cloud data platforms such as AWS Data Lake.
3+ years of experience with Data Governance, data classification, and data security.
2+ years of experience with Data Lineage, integration, and transformation tools, such as: Informatica, Fivetran, or similar tools.
2+ years of experience designing and implementing APIs for: Metadata harvesting, Automation, Event-driven integration.
2+ years of experience using GitHub.

Nice To Haves

2+ years of experience with vector databases and/or feature stores is preferred.
2+ years of experience with Python scripting is preferred.
2+ years of experience working with Texas State Government agencies is preferred.
Experience working with Texas State Government agencies.
Experience implementing enterprise MDM/Data Catalog solutions in large organizations.
Experience with AI/ML and generative AI data enablement.
Experience with semantic/context layers and metadata-driven AI discovery.
Experience with vector databases and feature stores.
Strong understanding of enterprise data governance and security.
Strong communication, analytical, and problem-solving skills.

Responsibilities

Design and implement an enterprise-wide Data Catalog and MDM solution for structured and unstructured data.
Define and configure enterprise data catalog architecture and metadata management processes.
Design and implement semantic/context layers to support analytics, AI/ML, and generative AI use cases.
Establish and maintain data lineage across enterprise data sources and platforms.
Develop processes for metadata harvesting and automated catalog updates.
Ensure data assets are properly documented, classified, governed, and secured.
Define data governance standards and processes to maintain data quality, consistency, and accuracy.
Integrate data catalog and MDM platforms with enterprise data sources and cloud platforms.
Design APIs and event-driven integrations for metadata harvesting, automation, and system synchronization.
Work with data engineering, governance, security, analytics, and AI/ML teams to understand data consumption requirements.
Support AI tools in discovering and consuming trusted enterprise data through appropriate semantic and context layers.
Implement continuous processes to keep metadata and catalog information current as enterprise systems change.
Design and support data models across Oracle and SQL Server environments.
Work with AWS and Azure cloud technologies and enterprise data platforms.
Evaluate and implement solutions involving vector databases and feature stores, where applicable.
Develop Python scripts and automation solutions to support data management and metadata processes.
Maintain technical documentation, architecture standards, data models, lineage documentation, and governance artifacts.
Use GitHub for source control and collaboration.
Collaborate with stakeholders and technical teams to deliver scalable, secure, and well-governed enterprise data solutions.