Data Enterprise Architect - Banking

Requirements

• - 10-12+ years of experience in data engineering, data architecture, or platform engineering, including significant experience in client-facing consulting, solution architecture, or program delivery leadership.
• - Proven experience designing and implementing enterprise-scale data platforms (lake/lakehouse/warehouse) on cloud and/or hybrid environments.
• - Strong foundation in data modeling, integration patterns, distributed processing, and performance tuning, with the ability to apply these to AI-ready data products (training/inference readiness, feature readiness, and observability).
• - Hands-on understanding of governance disciplines: data quality, catalog/metadata, lineage, privacy, retention, and access controls.
• - Working knowledge of AI-enabled engineering practices and/or MLOps fundamentals (model lifecycle, evaluation, monitoring), and how they intersect with data platform architecture and governance.
• - Excellent consulting communication skills: facilitate workshops, synthesize ambiguity into options/trade-offs, and present recommendations to technical and non-technical audiences.
• - Ability to lead cross-functional stakeholders, communicate architecture decisions, and influence senior leaders

Additional Information

We are seeking an Enterprise Data Architect to lead client-facing consulting engagements that define enterprise-wide data architecture and guide the delivery of modern data platforms that enable analytics, AI/ML, and digital products at scale. The ideal candidate blends data architecture and applied AI, with demonstrated ability to embed AI into the data engineering lifecycle (requirements discovery, design, development, testing, operations, and governance) to improve delivery speed, quality, reliability, and cost across multiple client contexts. You will work directly with client leadership to shape strategy, create roadmaps, architect solutions, and govern execution from proposal through implementation.

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Company Intel

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