Senior AI Data Platform Engineer - Snowflake

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all. 

EY-Consulting - Data and Analytics - AI Data Platform Engineer - Snowflake - Senior

EY's Consulting Services is a unique, industry-focused business unit that provides a broad range of integrated services that leverage deep industry experience with strong functional and technical capabilities and product knowledge. EY's financial services practice provides integrated Consulting services to financial institutions and other capital markets participants, including commercial banks, retail banks, investment banks, broker-dealers & asset management firms, and insurance firms from leading Fortune 500 Companies. Within EY's Consulting Practice, Data and Analytics team solves big, complex issues and capitalise on opportunities to deliver better working outcomes that help expand and safeguard the businesses, now and in the future. This way we help create a compelling business case for embedding the right analytical practice at the heart of client's decision-making.

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.

What we look for

• 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.

What working at EY offers

At EY, we're dedicated to helping our clients, from start-ups to Fortune 500 companies, and the work we do with them is as varied as they are.

You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasises high quality and knowledge exchange. Plus, we offer:

• Support, coaching and feedback from some of the most engaging colleagues around
• Opportunities to develop new skills and progress your career
• The freedom and flexibility to handle your role in a way that's right for you

EY | Building a better working world 

 
EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets.  

 
Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.  

 
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