Senior Data Architect

Modernizing and scaling enterprise data infrastructure requires sophisticated architecture that bridges traditional data modeling with contemporary lakehouse frameworks. This role resolves complex data fragmentation, storage tiering inefficiencies, and compute cost overruns across cloud environments to establish performant, enterprise-grade data assets. Crucially, this work unlocks advanced AI capabilities by designing architectures for unstructured data, vector databases, Retrieval-Augmented Generation (RAG), and semantic layers.

Through cross-functional governance and technical leadership, enterprise data assets become securely managed, highly scalable, and fully aligned with strategic goals.

What Success Looks Like (Objectives):

Establish and maintain corporate data model design principles; integrating Kimball and Inmon methodologies with Medallion Lakehouse patterns to deliver performant data assets

Architect scalable frameworks and storage tiering to support AI initiatives; including vector databases, Retrieval-Augmented Generation (RAG), and enterprise semantic layers

Lead efforts in optimizing query execution, cluster compute, and cloud warehouse costs across enterprise platforms

Partner with enterprise architects, data engineering, and business analysis teams to enforce data governance standards and execute data SDLC phases

Define data integration solutions and mitigate infrastructure risks to deliver scalable, highly available enterprise data architecture

Core Skills, Competencies, and Key Experiences (What you’ll bring)
Advanced AI Application expertise; leveraging unstructured data frameworks, vector databases, and Retrieval-Augmented Generation (RAG) within modern data architectures

Critical experience in designing multi-tier enterprise data structures, Medallion patterns, and high-performance data lakehouse architectures

Strong skills in data modeling, schema design, and versioning within multi-modeler repository environments

Critical experience in cloud warehouse cost optimization, cluster compute tuning, and query performance enhancement

Expertise in data governance, fine-grained access control (ABAC), data lineage, and compliance standards such as CPNI, CCPA, and SOC2

Exceptional problem solving, collaboration, and cross-functional decision-making capabilities across technical and business stakeholders

Additional Qualifications:

Preferred industry certifications including Snowflake Certified Architect, Databricks Professional, or AWS Data Engineer Candidates must be willing to participate in at least one in-person interview.

Minimum Requirements:

Minimum Education:

Bachelor's degree in a technical discipline, preferably in Computer Science or Software Engineering, or equivalent combination of education and experience

Minimum Experience:

10 years of continuous data architecture and data modeling experience; with at least 3 recent years leading primary data architecture design and implementation in a VLDB / MPP environment

Required Technical

Skills:

2 years of experience with Azure, AWS, or GCP cloud environments1 year of experience with Databricks & Genie and/or Snowflake & Cortex1 year of experience with orchestration tools such as Apache Airflow or Control-MVisa sponsorship not available for this position#LI-CH2

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