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 like 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).

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or related field and at least 7 years of experience in data architecture, enterprise data management, or large-scale data engineering, OR equivalent combination of education and work experience.
Proven experience designing and implementing enterprise data platforms supporting AI/ML initiatives.
Strong 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.

Paid Time Off
401(k) matching program
Annual incentive pay
Paid holidays
Comprehensive company sponsored benefit plan including medical, dental, vision, and other insurance coverage
Health savings, flexible spending, and dependent care accounts
Adoption assistance
Employee assistance program
Fitness reimbursement
Tuition reimbursement
Associate wellbeing program
Associate emergency fund
Various associate banking benefits

AI Cloud Data Engineer

Key skills