Snowflake Data Engineer

Experience Guide

• 4-10 years

Your key responsibilities

• Snowflake Data Engineering
• Design and implement scalable ELT/ETL frameworks using Snowflake, SQL, Snowpark Python, Dynamic Tables, Streams, Tasks, and Snowpipe.
• Develop ingestion pipelines supporting batch, event-driven, CDC, streaming, API-based, and third-party service ingestion patterns.
• ering, query performance, storage design, and cost efficiency.
• Experience with Aladdin.
• Snowflake Platform Engineering
• Develop reusable platform patterns for onboarding, database/schema standards, pipeline templates, logging, monitoring, cost controls, and operational support.
• Implement Git connectivity, branching strategy, pull requests, code reviews, CI/CD, Infrastructure as Code, release automation, and environment promotion.
• Integrate Snowflake with enterprise APIs, source systems, orchestration platforms,
• Cortex AI & Agentic Enablement
• Implement Cortex AI Functions, Cortex Search, Cortex Analyst, Cortex Agents, vector search, semantic retrieval, RAG, and conversational BI patterns.
• Enable AI-powered data discovery, enterprise search, contextual exploration, and AI-ready data products over governed Snowflake datasets.
• Apply agentic operations for anomaly detection, query/failure diagnosis, data quality recommendation, documentation generation, and incident summarisation.
• Governance, Security & Data SRE
• Implement RBAC/ABAC, masking policies, row/column-level security, tags, classification, lineage, audit logging, secrets management, and policy-as-code.
• Integrate with Immuta, Snowflake Horizon, Microsoft Purview, Collibra, IAM, monitoring