EY - GDS Consulting - AI And DATA - Snowflake-Senior

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EY GDS – AI & Data     Snowflake Senior Data Engineer     AI-enabled data platform engineering       The opportunity   Our AI & Data practice helps clients build trusted, modern data platforms that power analytics, operational decisions, and responsible AI. As a Snowflake Senior Data Engineer, you will own end-to-end data products and technical workstreams, from design through production operation. You will combine strong engineering fundamentals with practical use of native AI capabilities on Snowflake, always prioritising business value, security, and reliable production operation.     Your key responsibilities   Data engineering and platform delivery

Design and build secure, performant data products on Snowflake using warehouses, dynamic tables, Snowpark, streams, tasks, Snowpipe, and domain-aligned data modelling. Implement data ingestion, transformation, orchestration, testing, and performance optimization using SQL, Python, Snowpark, dbt or comparable engineering patterns. Design and own scalable batch and streaming data products, selecting appropriate data models, processing patterns, quality controls, and service-level expectations. Troubleshoot complex performance, reliability, and cost issues; establish reusable engineering patterns and guide code reviews and delivery standards. Translate business requirements into reusable, tested data products with clear ownership, contracts, documentation, data-quality checks, and service-level expectations.

AI-ready data and native AI capabilities

Use Snowflake Cortex capabilities—such as Cortex AI functions, Cortex Analyst, Cortex Search, Cortex Agents, and Snowflake Intelligence—where they provide a governed native AI path. Design AI-ready data products and implement production patterns for retrieval, semantic search, evaluation, safety, and reliable operation. Assess when a governed RAG or agent workflow is justified and implement the supporting ingestion, metadata, access-control, evaluation, and deployment foundations. Develop and test scoped system prompts, context-assembly patterns, agent skills, and approved MCP integrations with clear tool contracts, least-privilege access, input/output validation, and traceability. Partner with data scientists, analytics teams, security, and business stakeholders to select the right pattern: deterministic analytics, semantic layer, retrieval-augmented generation (RAG), agent workflow, or model-based solution. Ensure AI solutions have documented data sources, access controls, quality thresholds, evaluations, human oversight where needed, and clear release controls.

Governance, security, and engineering excellence

Apply Snowflake governance, Horizon Catalog / lineage capabilities, RBAC, masking policies, row access policies, data quality controls, and cost management. Implement automated testing, source control, code reviews, CI/CD, release controls, monitoring, alerting, incident learning, and clear runbooks. Work in Agile teams and communicate progress, dependencies, risks, and design decisions clearly to technical and non-technical stakeholders. Lead technical delivery for a workstream, mentor engineers, and communicate design choices and delivery risks to client stakeholders.

Skills and attributes for success

Advanced SQL plus Python; Snowpark and PySpark experience are required, including the ability to compare appropriate execution engines and integration patterns. Data modelling, query profiling, warehouse sizing, resource monitors, CI/CD, Git, and deployment automation. Practical experience