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Tier-1 brand and Bangalore metro increase density; senior, niche AI focus reduces applicant pool.
Core data engineering skills transferable, but LLM/vector and Snowflake specialization increases domain specificity.
Explicit 10+ years, 4+ years data-engineering, Snowflake and pipeline mandates make screening highly selective.
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Design and optimize Snowflake data pipelines and semantic data layers specifically for AI use cases, focusing on retrieval and agent workflows.
Ensure data freshness, quality, lineage, and compliance to enable trustworthy, explainable AI agents with governed access and privacy safeguards.
Collaborate with AI platform, data governance, and FinOps teams to maintain secure, cost-efficient AI-ready data infrastructure and products.
10+ years overall experience with at least 4+ years in data engineering focusing on Snowflake expertise (modeling, performance, security).
Advanced proficiency in SQL and Python for data pipeline development with experience using modern data stack tools (dbt, Airflow, fivetran).
Experience integrating Snowflake with GCP services and familiarity with building production data pipelines focused on AI workloads (embeddings, vector storage, RAG).
Work Experience Required: 10+ years overall, 4+ years in data engineering
Proven ability to build and govern AI-focused data products that improve reliability and grounding of autonomous agents.
Experience working in cross-functional environments integrating data engineering with AI/ML platforms, security, and finance teams.
Strong focus on technical excellence in data freshness, quality, governance, and cost optimization specifically for AI data pipelines and semantic modeling.