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Tier-1 brand and Bangalore metro raise applicant density, but seniority and specialized Snowflake/AI skills moderate competition.
Specialized Snowflake, RAG, and production data pipeline expertise yields strong domain-specific hiring bias.
Explicit 10+ years, mandatory Snowflake and AI pipeline skills create stringent candidate filters.
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Build and maintain AI-optimized semantic data layers and pipelines in Snowflake to support autonomous agent workflows with fresh, reliable, and structured data.
Ensure data quality, lineage, governance, and compliance for AI use cases, including access controls and privacy standards for safe large-scale deployment.
Collaborate with cross-functional teams to integrate Snowflake with GCP services, monitor pipeline cost/performance, and deliver reusable data products improving agent accuracy and trustworthiness.
10+ years total professional experience with at least 4 years in data engineering focused on Snowflake (modelling, performance, security).
Strong advanced SQL and Python skills for data pipeline development; experience with modern data stack tools like dbt and Airflow.
Understanding of retrieval-augmented generation (RAG) data preparation techniques including embeddings, vector stores, and metadata handling.
Work Experience Required: 10+ years overall, 4+ years relevant data engineering; Notice Period: Not explicitly mentioned in the JD.
Experienced senior data engineer with demonstrated expertise in building AI/ML-focused data pipelines and semantic data products for LLM and autonomous agent use cases.
Proven ability to operationalize and govern AI data with a strong focus on data quality, lineage, privacy, and compliance within enterprise cloud environments (Snowflake and GCP).
Skilled in collaborating closely across AI platform, governance, and financial operations teams to optimize data freshness, reliability, retrieval quality, and cost efficiency.