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Tier-1 brand plus metro hiring but senior, specialized role reduces general applicant density.
High: requires finance domain expertise, regulated reporting experience, Snowflake and enterprise LLM orchestration skills.
High: explicit 10+ years, finance domain mandate, Snowflake and LLM/RAG expertise, plus leadership and governance requirements.
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Lead design and implementation of enterprise-scale finance data platforms, including data warehousing, reporting, analytics, and AI-powered capabilities.
Drive integration and adoption of GenAI, LLM orchestration, RAG architectures, and agent-based AI workflows within finance data environments.
Provide technical leadership and mentorship while ensuring AI solutions meet security, governance, and responsible AI standards in regulated finance context.
10+ years of experience in data engineering or data architecture with at least 6 years in relevant skills for this role.
Strong domain expertise in finance, investment banking, or related financial industries is mandatory.
Demonstrated hands-on experience with cloud data platforms preferably Snowflake and designing AI/GenAI enterprise solutions including AI orchestration and conversational data interfaces.
Work Experience Required: 10+ years in data engineering/data architecture; Notice Period: Not explicitly mentioned in the JD.
Experienced leader capable of managing complex AI-enabled data engineering projects in a global, regulated finance environment.
Strong blend of deep technical skills in data engineering combined with applied AI/GenAI architecture expertise, especially around RAG, LLMs, and enterprise AI integration.
Skilled in driving technical innovation, mentoring teams, and collaborating cross-functionally to align data and AI strategies with business objectives.