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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 implementing or supporting RAG, semantic search, agent workflows, context engineering, system prompts, agent skills, MCP integrations, or LLM evaluation would be an added advantage.
- Strong analytical problem solving, written and verbal communication, and a consulting mindset grounded in measurable client outcomes.
- Experience with API-based integrations, data contracts, security-by-design, and cloud-native identity, networking, and secrets-management concepts.
- Ability to explain trade-offs between batch, streaming, SQL, Python, PySpark, platform-native AI, and external AI services.
Experience and qualifications
- 5–8 years of relevant data engineering, analytics engineering, platform engineering, or comparable consulting experience.
- Bachelor’s or master’s degree in computer science, engineering, information systems, data science, or a related discipline; equivalent practical experience will be considered.
- Demonstrable delivery of production data solutions using Python, PySpark, and SQL. Experience with modern data modelling, distributed processing, and cloud data security is essential.
- SnowPro Core or advanced Snowflake certification is preferred.
Ideally, you will also have
- Experience in financial services, consumer, healthcare, public sector, or another regulated industry.
- Experience with data mesh, domain ownership, event-driven architecture, or FinOps practices.
- Evidence of building accessible, inclusive teams and improving engineering practices through reusable accelerators, standards, or coaching.
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