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Tier-1 brand plus Bangalore metro and broad multi-cloud skill requirements yield moderate competition density.
Core data architecture skills are transferable, but enterprise-scale and agentic AI experience increases domain specificity.
Many mandatory technical stacks, cloud architecture and architecture-level experience make screening highly selective.
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Design and own end-to-end scalable, governed data and analytics architectures across data lakes, warehouses, BI, ETL pipelines, and AI-driven solutions.
Architect and optimize data pipelines and solutions for enterprise-scale use cases across Azure and GCP ecosystems with a strong AI-readiness focus.
Lead technical governance, mentor teams, collaborate with stakeholders to translate business needs into impactful data products with measurable outcomes.
Strong hands-on expertise in Python, Databricks ecosystem, SQL/PostgreSQL, Apache Airflow, Power BI, and experience with GCP and Azure services.
Experience designing and delivering scalable, production-grade data and AI solutions using cloud-native tools in Azure and GCP.
Work Experience Required: Not explicitly mentioned in the JD.
Location: Bangalore, India (onsite/remote not explicitly mentioned).
Experienced in architecting integrated data solutions combining data lakes, warehouses, BI, and AI in cloud ecosystems (Azure and GCP).
Skilled in designing agentic AI and generative AI architectures with practical use of frameworks like LangChain or AutoGen, focused on enterprise AI adoption.
Proven ability to lead technical teams, enforce best practices in data governance, and translate complex business requirements into deployable analytics and AI solutions.