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Tier-1 brand and metro location increase applicant density, seniority requirement reduces but still competitive.
Strong platform and GenAI skills are transferable, but regulated financial experience preference increases domain sensitivity.
Rigid 15+ years, deep cloud, Python/SQL, production ML/GenAI and platform experience mandate strict filters.
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Architect, build, deploy, and operate scalable enterprise data pipelines supporting analytics, ML, and GenAI workloads.
Use AI/ML and GenAI techniques to design, optimize, and automate data engineering workflows and pipeline operations.
Provide senior technical leadership including architecture reviews, mentoring, and setting standards for data and AI engineering at enterprise scale.
15+ years experience owning and operating large-scale data engineering platforms in production.
Strong hands-on expertise in Python and SQL with deep experience on cloud data platforms (AWS, Azure, or GCP).
Proven architecture experience for distributed, high-performance data systems with AI/ML or GenAI pipeline design and optimization experience.
Work Experience Required: 15+ years in data engineering; Notice Period: Not explicitly mentioned in the JD.
Experienced in integrating AI/ML and GenAI to transform and automate data pipeline lifecycle and operational workflows.
Capable of leading senior-level engineering teams using a player-coach approach and working in large, cross-functional agile environments.
Has prior exposure to financial services or regulated environments and able to apply enterprise standards for reliability, security, and compliance.