Director of Data Engineering and Artificial Intelligence Platforms

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.