Senior AI Data Platform Engineer - Snowflake

EY; Bengaluru; On-site; INR 4,500,000 - 7,000,000; Full time

Job summary

EY in Bengaluru seeks an AI Data Platform Engineer focused on Snowflake AI Data Cloud to design and operate enterprise-grade data & AI platforms. You will leverage Snowflake Data Cloud, Snowpark, Dynamic Tables, and Cortex components to deliver scalable data products with governance and security in mind.

Responsibilities include building reusable platform patterns, implementing CI/CD, and integrating with enterprise APIs, to enable AI-ready datasets and informed decision-making across the

Qualifications

• 5–10 years in data engineering or platform engineering.
• Hands-on with cloud data platforms, APIs, and Git-based delivery.
• Certifications in cloud, data engineering, DevOps, security, or AI/ML are preferred.

Responsibilities

• Design and implement scalable ELT/ETL with Snowflake, Snowpark Python.
• Develop ingestion pipelines for batch, streaming, CDC, and API sources.
• Create reusable platform patterns and governance-ready datasets.
• Collaborate on security, SRE, and production reliability.

Skills

Snowflake Data Engineering

Snowpark Python

Dynamic Tables

Streams & Tasks

ETL/ELT Frameworks

Semantic Data

Platform Engineering

Git & CI/CD

Governance & Security

API Integration

Tools

Snowflake

Snowpark

dbt

GitHub Actions

Terraform/OpenTofu

Kubernetes

CI/CD tooling

Immuta/Purview

Job description

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 clear 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.