Director - Data Engineering And AI Platforms

Desired Qualifications

• Experience in financial services, with understanding of consumer and commercial banking data.
• Experience supporting or enabling AI/ML and GenAI solutions, including feature pipelines and analytics platforms.
• Familiarity with data visualization and BI tools (Tableau, Cognos, SAS).
• Knowledge of responsible AI, data governance, and regulatory considerations in highly regulated environments.
• Experience modernizing legacy data platforms into cloud-native architectures.
• Executive speaking skills — ability to articulate strategy, challenge the status quo, and present to senior leadership and key stakeholders with confidence.
• Experience working across or within highly collaborative, non-hierarchical organizational cultures with an emphasis on peer relationships and open communication. Data Engineering & Architecture
• Serve as a hands-on technical leader in the design of scalable data pipelines, data stores, and information flows across the enterprise.
• Design and optimize cloud-based big data platforms, including ingestion, transformation, storage, and consumption layers.
• Lead the engineering of ETL/ELT frameworks, streaming pipelines, and batch processing solutions.
• Conduct enterprise-wide assessments of data stores and data flows to identify bottlenecks, friction points, and modernization opportunities.
• Own data modeling standards to ensure alignment with business objectives, performance, and accessibility. AI Enablement & Advanced Analytics
• Enable and support AI/ML and GenAI initiatives by building reliable, high-quality, and well-governed data pipelines.
• Collaborate with Data Science teams to operationalize models, including feature engineering pipelines, inference data flows, and model monitoring data.
• Support AI-driven use cases such as predictive analytics, recommendations, NLP-based insights, and intelligent automation.
• Stay current with market trends, embed innovative practices into strategy, and drive the organization forward with an AI-first approach — ensuring AI initiatives move beyond proof-of-concept to enterprise-scale solutions.
• Approach data engineering with an AI mindset and vice versa, reflecting the evolving and inseparable nature of the two disciplines.