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Tier-1 bank brand attracts applicants, but senior and niche GenAI/data requirements limit candidate pool.
Mandatory finance domain expertise and regulated environment make cross-industry transferability low.
Explicit 10+ years, mandatory finance domain knowledge, and specific GenAI/Snowflake expertise enforce strict filters.
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Lead design, architecture, and implementation of enterprise-scale data platforms including data warehousing, reporting, analytics, and AI-powered data solutions on cloud platforms like Snowflake.
Drive adoption and integration of GenAI, LLMs, RAG architectures, and AI-enabled natural language querying for finance data platforms.
Provide technical leadership, mentor data engineering teams, and collaborate with stakeholders to define and deliver data and AI strategies aligned with business goals.
10+ years of experience in data engineering, data architecture, or related roles with enterprise-level solution delivery.
Strong domain experience in finance, investment banking, or related industries mandatory.
Proven expertise in cloud data platforms (preferably Snowflake) and AI/GenAI technologies including RAG, LLM orchestration, prompt engineering, and AI-enabled data pipelines.
Work Experience Required: 10+ years data engineering with at least 6 years relevant to AI and finance domain.
Experienced leader capable of driving enterprise AI adoption and building intelligent, scalable data products in regulated finance environments.
Proficient in integrating AI/GenAI capabilities with enterprise data platforms, focusing on secure, governed, and responsible AI solutions.
Comfortable operating at the intersection of advanced data engineering, AI architecture, and strategic stakeholder management in a global financial institution.