Data Engineer Coordinator – AI Engineering

Job not on LinkedIn

🔥 14 hours ago

🇧🇷 Brazil – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🚰 Data Engineer

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Nestle

WebsiteLinkedInAll Job Openings

5001 - 10000 employees

🍽️ Food & Beverage

🏭 Manufacturing

📦 Logistics

Food & Beverage • Manufacturing • Logistics

Nestle is a Swiss multinational food and beverage company that produces and markets a wide range of packaged foods and beverages, including coffee, bottled water, dairy products, infant nutrition, confectionery, frozen foods, and pet care brands. The company sells its products globally through retail and foodservice channels, operates extensive manufacturing and supply-chain operations, and focuses on nutrition, health, and wellness initiatives.

📋 Description

• Design, develop, and evolve data solutions in modern analytics environments. • Serve as a technical reference for strategic initiatives and support the adoption of data engineering best practices. • Design and implement enterprise agentic solutions, including LLM orchestration, reasoning workflows, external tools, and integration with APIs and company data. • Design Retrieval-Augmented Generation (RAG) pipelines, including indexing, retrieval, and context enrichment. • Enhance agents to query knowledge bases and analytical and enterprise systems with authentication and access controls. • Ensure observability, inference cost management, LLM selection, and security in cloud applications. • Develop and optimize scalable, secure, and high-performance data pipelines. • Provide technical leadership for data engineering initiatives using Snowflake, Databricks, and cloud platforms. • Define and apply best practices for development, testing, observability, and automation. • Contribute to data modeling and the development of reusable, sustainable analytics solutions. • Identify opportunities to improve performance and operational efficiency and optimize costs. • Support technical reviews and mentor less experienced engineers. • Ensure compliance with security, governance, and data quality standards. • Partner with business, analytics, and technology teams to define data solutions. • Contribute to the implementation of AI Engineering capabilities by integrating generative AI applications, intelligent agents, and solutions based on enterprise data.

🎯 Requirements

• Strong experience in Data Engineering and implementing analytics solutions at scale. • Advanced knowledge of Snowflake, including data modeling, query optimization, performance tuning, security, and cost management. • Experience with Databricks and modern cloud data architectures. • Proficiency in advanced SQL and data pipeline development. • Experience with Python for automation, data processing, and integration. • Experience with dbt or equivalent tools for data transformation and modeling. • Knowledge of CI/CD, version control, and deployment automation practices. • Knowledge of Data Governance, Data Catalog, and Data Quality. • Experience in Azure environments or equivalent cloud platforms. • Hands-on experience in AI Engineering, including consuming AI models through APIs, RAG, AI agents, and integrating AI solutions with enterprise data platforms. • Preferred knowledge: Snowflake Clustering, Materialized Views, Time Travel, Zero-Copy Cloning, Resource Monitoring, and Data Sharing. • Preferred knowledge: Databricks Spark, Delta Lake, Unity Catalog, Workflows, and Asset Bundles. • Preferred knowledge: Azure Data Factory, Data Lake Gen2, Azure Functions, and integration services. • Preferred knowledge: GitHub, Azure DevOps, and DevSecOps practices. • Preferred knowledge: Data Catalog, Data Lineage, Data Observability, and Metadata Management. • Preferred knowledge: AI Engineering, LLMOps, and Agentic AI frameworks and tools.

🏖️ Benefits

• Remote work