Description

Define and maintain the enterprise data architecture for AI and machine learning projects, ensuring scalable foundations for real-time and batch workloads.

This role is on-site.

Responsibilities

• Design data models, schemas, and pipelines optimized for AI/ML consumption across Snowflake, BigQuery, Postgres, and supporting platforms.
• Architect integration patterns that connect structured and unstructured data sources (Salesforce, SharePoint, PDFs, APIs) into unified, AI-ready datasets.
• Build and maintain a comprehensive data catalog and lineage map to document data assets, usage, and AI model dependencies.
• Establish standards for data ingestion, transformation, and storage that support feature stores and vector embeddings.
• Collaborate with AI/ML engineers and platform teams to translate business requirements into data architecture decisions.

Required Skills

• 8+ years of experience in data architecture, data engineering, or enterprise data management.
• 3+ years of hands-on experience designing data solutions for AI/ML workloads.
• Deep expertise with cloud data platforms, particularly Snowflake, Google BigQuery, or equivalent.
• Experience with enterprise data lake or data mesh architectures.
• Strong understanding of data modeling (dimensional, graph, document), ETL/ELT pipelines, and data integration patterns.
• Experience with data governance frameworks, data cataloging tools, and lineage tracking.
• Proficiency in SQL; working knowledge of Python or similar scripting languages.
• Bachelor’s degree in Computer Science, Data Science, Information Systems, or related field.

Preferred Skills

• Experience with Snowflake’s advanced features (Snowpark, Cortex, data sharing, dynamic tables).
• Hands-on experience with GCP services (Vertex AI, Cloud Storage, Pub/Sub, Dataflow).

Key Skills

Education

Bachelor's degree