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Tier-1 brand and Bangalore location increase applicant density, but senior specialized skillset narrows the pool.
Deep enterprise data architecture and multi-cloud expertise required, limiting cross-domain transferability.
Extensive mandatory technical stack and enterprise architecture expectations imply strict qualification filtering.
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Design and own end-to-end data and analytics architectures across data lakes, warehousing, BI, ETL pipelines, and AI-driven solutions ensuring scalability, governance, and AI readiness.
Architect, optimize, and standardize data pipelines and data models for reliability, performance, and self-service capabilities across Azure and GCP ecosystems.
Lead AI and advanced analytics enablement incorporating agentic and generative AI patterns, and drive solution alignment with business requirements and enterprise cloud strategy.
Strong hands-on expertise in Python, Databricks ecosystem, SQL/PostgreSQL, Apache Airflow, Power BI, and GCP services including Cloud Run.
Experience with Azure ecosystem (ADF, Azure SQL) and multi-cloud architectures combining Azure and GCP.
Work Experience Required: Not explicitly mentioned in the JD.
Hands-on experience in web/backend app development using modern frameworks (Node.js, React, .NET, Python FastAPI) and AI/ML solution design including agentic AI frameworks such as LangChain or AutoGen.
Senior-level technical leader skilled in designing scalable, modular enterprise data and AI architectures integrating across Azure and GCP platforms.
Experienced in delivering production-grade data, analytics, and AI-driven products with measurable business impact in data-intensive or digital-first environments.
Capable of translating complex business requirements into standardized, governable, and cost-effective data solutions while mentoring teams and enforcing data governance and security standards.