Back
Tier-1 brand and Mumbai metro increase competition, but seniority and niche GenAI/finance focus reduce applicant density.
Requires deep finance domain knowledge and Snowflake/GenAI expertise, limiting cross-industry transferability.
Explicit 10+ years, finance-domain mandate, and specialized GenAI/Snowflake requirements enforce strict shortlisting.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead architecture, design, and implementation of enterprise-scale data warehousing, reporting, analytics, and AI-powered data solutions, focusing on finance data platforms.
Drive adoption and integration of GenAI, LLMs, retrieval-augmented generation, agent-based AI workflows, and natural language data interaction capabilities within enterprise data systems.
Provide technical leadership and mentorship to data engineering teams, govern AI solution evaluation frameworks and ensure AI governance, security, and compliance in financial services environments.
10+ years of experience in data engineering, data architecture, or related roles with enterprise-level solution delivery.
Strong expertise in SQL, data modeling, ETL, and scalable data pipeline development; hands-on experience with cloud data platforms, preferably Snowflake.
Mandatory domain knowledge of finance, investment banking, or related financial industries.
Demonstrated experience designing and implementing enterprise AI/GenAI solutions including RAG architectures, LLM orchestration, prompt engineering, and AI governance controls.
Experienced leader operating at the intersection of advanced data engineering and AI architecture within regulated finance environments.
Proven ability to innovate by integrating GenAI and AI-powered natural language interaction capabilities into large-scale finance data platforms.
Capable of managing complex projects and stakeholder relationships globally, while mentoring teams and championing modern agile and CI/CD practices for AI and data engineering.