Data Architect

Artificial Intelligence (AI) team is responsible for driving automation, data-driven decision making and software modernization using AI centric tools. Currently, we leverage machine learning, natural language processing, predictive analytics, and generative AI to enhance customer experience, and improve operational efficiency.

This includes utilizing fraud protection software to ensure regulatory and system availability.

As AI Data Architect, you will be responsible for designing and implementing enterprise-scale data architectures that enable secure, scalable, and compliant adoption of AI across the financial organization. You will bridge advanced data engineering, governance, and AI/ML model lifecycle requirements to ensure reliable, ethical, and regulatory-compliant use of data.

You will also provide strategic leadership in modernizing data ecosystems, enabling advanced analytics, and aligning AI-ready data platforms with business priorities in payments, risk management, compliance, and customer experience. This is a subset of the overall responsibilities which will include multiple initiatives as assigned by IT leadership.

How you’ll spend your time

• 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.

We’re excited to talk if you have

• 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.

Bonus Points if you have

• Master’s degree in Data Science, Information Management, or related discipline and at least 10 years of experience in financial services or regulated industries.
• Certifications such as AWS Certified Data Analytics – Specialty, Google Professional Data Engineer, or DAMA CDMP.
• 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).

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