AI Data Architect

Responsibilities

• Define and implement enterprise-wide AI data architecture strategies to enable secure, scalable, and compliant AI/ML adoption across the organization.
• Design canonical data models, metadata frameworks, and pipelines to support AI/ML model development, training, deployment, and monitoring.
• Establish standards for data quality, lineage, master data management (MDM), and “golden record” frameworks to ensure reliable AI outputs.
• Collaborate with data governance, compliance, and security teams to enforce policies for ethical and responsible AI data usage.
• Architect and oversee AI-ready data platforms (Cloud, hybrid, and on-prem) to integrate structured, unstructured, and streaming data sources.
• Implement modern data architectures (e.g., Data Mesh, Data Fabric, Lakehouse, Apache Iceberg/Delta Lake) to accelerate AI/ML projects.
• Optimize AI/ML workloads on Cloud platforms (AWS, Azure, GCP) and manage data pipelines using tools including Kafka, Glue, Spark, and Snowflake.
• Partner with AI engineers, data scientists, and business leaders to align data architecture with financial products, regulatory compliance, and risk management needs.
• Provide architectural leadership in modernization efforts (real-time payments, fraud detection, personalization engines, and regulatory reporting).

Requirements

• 7 years of experience in data architecture, enterprise data management, or large-scale data engineering.
• Proven success designing and implementing enterprise data platforms supporting AI/ML initiatives.
• Solid understanding of data governance, regulatory requirements, and compliance in financial services.
• Experience partnering with cross-functional teams to deliver enterprise-grade data solutions.
• Expertise in enterprise data architecture frameworks, canonical models, and metadata management.
• Knowledge of AI/ML lifecycle data requirements including model training, validation, and monitoring.
• Proficiency with Cloud data platforms (AWS Redshift, Snowflake, Azure Synapse, GCP BigQuery).
• Familiarity with big data and streaming platforms (Kafka, Spark, Flink).
• Strong skills in database technologies (SQL, NoSQL, graph databases) and data integration patterns.

Preferred Certifications

• AWS Certified Data Analytics – Specialty
• Google Professional Data Engineer
• DAMA CDMP

Experience

• Regulatory reporting, risk data aggregation, and AI-specific compliance frameworks experience.
• Experience presenting and defending architectural decisions with senior executives and regulators.
• Hands-on experience with data Lakehouse technologies (Apache Iceberg, Delta Lake, Hudi).
• Advanced knowledge of MDM, data mesh, and distributed architectures.
• Experience with AI observability and monitoring tools for data pipelines.
• Familiarity with privacy-preserving technologies (differential privacy, secure multiparty computation).
• Deep understanding of AI-driven data applications in banking (fraud detection, personalization, regulatory reporting).

Benefits

100% Paid employee Medical/Dental Benefits, Paid time off, Paid Holidays, and 401(k)(with immediately-vested company match) available with TriCom during the contract period.