Job Summary

Role AI Data Platform Engineer - Snowflake

Experience Guide

- 5-10 years

Primary Skill Area
- Cortex AI, Semantic Data, Snowpark & Modern Data Platforms

The opportunity Build and operate enterprise-grade Data & AI platforms using Snowflake AI Data Cloud. The role focuses on Snowflake data engineering, reusable platform patterns, semantic data products, governed AI-ready datasets, APIs, enterprise service integration, Git-based delivery, Data SRE, data security, Immuta-style governance, and agentic automation powered by Snowflake Cortex, Snowpark, Cortex Search, Cortex Analyst, Cortex Agents, Dynamic Tables, Streams, Tasks, and Snowpipe.

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.
- Build curated, analytics-ready, and AI-ready data products with transparent ownership, quality controls, semantic context, and consumption patterns.
- Optimise Snowflake workloads, virtual warehouse usage, clustering, query performance, storage design, and cost efficiency.

- 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, governance tools,



security services, and downstream analytics consumers.
- Support platform standards, technical design reviews, deployment governance, and production reliability.

- 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 tools, and enterprise access workflows.
- Build Data SRE dashboards covering pipeline health, warehouse usage, query performance, data quality, access activity, incidents, cost, and SLA/SLO adherence.

Skills and attributes for success Skill / capability area & Details
- Core platform - Snowflake AI Data Cloud, Snowpark, Dynamic Tables, Streams & Tasks, Snowpipe, Data Sharing, Native Apps,



semantic data products.
- Cortex AI and GenAI - Cortex AI Functions, Cortex Search, Cortex Analyst, Cortex Agents, Vector Search, RAG, GraphRAG, Agentic AI, semantic retrieval.
- Engineering -SQL, Python, Snowpark Python, APIs, Git, CI/CD, dbt, unit testing, integration testing, data pipeline testing, GitHub Copilot.
- Cloud and DevOps - AWS, Azure or GCP, Terraform/OpenTofu, Kubernetes, Docker, GitHub Actions, Azure DevOps, Jenkins, policy-as-code, any enterprise scheduling/orchestrations tools for ex Azure Data Factory
- Governance and reliability - RBAC/ABAC, Immuta, Purview, masking, row/column security, tags, classification, lineage, audit logging, data quality, Data SRE, FinOps.

To qualify for the role, you must have
- 5-10 years of experience in data engineering, data platform operations, analytics engineering, platform engineering, or AI platform enablement.
- Strong hands-on implementation experience with cloud data platforms, APIs, Git connectivity, CI/CD, governed access patterns, SRE practices, and production operations.
- Preferred certifications aligned to the relevant cloud/platform stack, data engineering, DevOps, security, governance, and AI/ML engineering.

Ideally, you'll also have
- Strong hands-on engineer with architecture awareness, delivery ownership, and a platform engineering mindset.
- Comfortable turning platform standards into reusable frameworks, secure implementation patterns, operational controls, and production-ready services.
- Able to mentor engineers, collaborate with architects/security/SRE teams, and adopt newer AI-native and agentic engineering methods.

📌 Senior AI Data Platform Engineer - Snowflake (Bengaluru)
🏢 EY
📍 Bengaluru