EY - GDS Consulting - AI And DATA -AI Data Platform Engineer
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Job Description
- 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.