Data Architect

POSITION SUMMARY

The Data Architect is responsible for designing and implementing enterprise data architecture solutions that ensure seamless flow of data across systems for our organization and our customers. This role requires a data professional capable of implementing modern data architectures while developing expertise in emerging technologies including AI/ML data pipelines, real‑time streaming architectures, and data infrastructure to support autonomous agent systems. The Data Architect will design, create, deploy, and manage the organization's data architecture while ensuring data assets are aligned with business goals, secured appropriately, and optimized for both traditional analytics and emerging AI/Agentic workloads.

• Design and implement secure, scalable, and efficient data architectures on cloud platforms, utilizing modern data lake, data warehouse, and Lakehouse patterns.
• Model datasets to support financial reporting initiatives, analytics, and growing AI/ML workloads.
• Implement data pipelines that support both batch and real‑time streaming requirements.
• Build data infrastructure capable of supporting AI/ML model training, inference, and autonomous agent data access.
• Gain hands‑on experience with vector database solutions and embedding storage for AI‑powered applications.
• Support data mesh and data product initiatives that enable self‑service data consumption.
• Learn and implement knowledge graph concepts and semantic layers that support AI reasoning.
• Create data integration processes ensuring seamless flow of data across enterprise systems.

• Work closely with analysts, data scientists, and stakeholders to understand data requirements and translate business needs into robust, scalable data models.
• Implement data governance and quality processes to ensure data integrity and compliance with regulatory standards.
• Support data cataloging, lineage tracking, and metadata management initiatives.
• Learn and apply responsible AI data practices including bias detection, fairness, and explainability considerations.
• Optimize data architectures for high performance, cost efficiency, and operational excellence.
• Collaborate with IT security teams to ensure data architecture aligns with security policies and data protection requirements.
• Collaborate with data engineers and analysts, sharing knowledge on architectural best practices.

• Collaborate with management, customers, analysts, and IT teams to understand structural requirements.
• Support the definition of standards for storing, consuming, integrating, and managing data across the organization.
• Translate business data requirements into usable architectural blueprints.
• Communicate effectively with stakeholders regarding project status, data implementations, and delivery time frames.

• Stay updated on the latest cloud data services, AI/ML data requirements, and emerging data technologies.
• Research data infrastructure requirements for Agentic Architecture and autonomous agent workloads.
• Evaluate vector databases, knowledge graphs, and semantic technologies for AI applications.
• Foster innovative team culture and process improvement during development phases.
• Contribute to organizational data governance and compliance frameworks.
• Other related duties as assigned.

• Technical
  
  Skills:
  
  
• Demonstrated experience with data modeling techniques and warehousing principles, specifically schema design.
• Demonstrated experience with cloud data platforms (e.g. Azure Data Lake, Synapse, Databricks, Snowflake, AWS Redshift, Big Query).
• Proficiency in SQL and programming languages such as Python, Scala, or Spark for data manipulation and transformation.
• Experience with real‑time data streaming platforms (e.g. Kafka, Kinesis, Event Hubs, Pub/Sub).
• Knowledge of data integration patterns including ETL/ELT, CDC, and real‑time synchronization.
• Familiarity with vector databases and embedding storage for AI applications, with willingness to learn.
• Experience with data governance tools, data catalogs, and metadata management platforms.
• Exposure to AI/ML data pipeline…