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.
