Data Engineer Coordinator (AI Engineering)

Job description

**This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer Coordinator (AI Engineering) based in Brazil.** This role combines advanced data engineering with the rapidly evolving field of AI Engineering, offering the opportunity to shape modern, scalable data and AI capabilities. You will serve as a technical reference for strategic initiatives, designing reliable data platforms and intelligent, agentic solutions. The position spans data architecture, Snowflake, Databricks, cloud platforms, RAG, LLMs, and AI agents. You will help build secure, observable, and cost-efficient solutions that connect enterprise data with generative AI applications. The role also involves establishing engineering standards, improving automation and performance, and supporting strong data governance. Working closely with technology, analytics, and business teams, you will help turn complex data and AI opportunities into sustainable enterprise solutions. ### Accountabilities: - Design and implement enterprise AI agent architectures, including LLM orchestration, reasoning workflows, external tools, APIs, and integration with corporate data. - Architect and develop Retrieval-Augmented Generation (RAG) pipelines covering indexing, retrieval, and context enrichment to improve accuracy and reduce hallucinations. - Build secure integrations and connectors that enable AI agents to interact with knowledge bases, analytical platforms, and corporate systems while maintaining strong authentication and access controls. - Drive LLMOps and production readiness by implementing observability, inference cost management, appropriate LLM selection, and cloud security practices. - Design, develop, and optimize scalable, secure, and high-performance data pipelines and analytical solutions. - Provide technical leadership for data engineering initiatives using Snowflake, Databricks, and modern cloud platforms. - Establish and promote best practices across development, testing, observability, automation, CI/CD, and deployment. - Lead data modeling efforts and develop reusable, sustainable solutions that support analytics and business needs. - Identify opportunities to improve performance, operational efficiency, scalability, and technology costs. - Conduct technical reviews and contribute to the development and mentoring of less experienced data engineers. - Ensure data engineering solutions comply with established standards for security, governance, quality, and reliability. - Partner with business, analytics, and technology stakeholders to translate requirements into effective data and AI solutions. - Contribute to the broader adoption of AI Engineering capabilities, including generative AI applications, intelligent agents, and solutions built on enterprise data. ### Requirements - Proven experience in data engineering and the delivery of analytical solutions at scale, with the ability to provide technical direction on complex initiatives. - Advanced expertise in Snowflake, including data modeling, query optimization, performance tuning, security, cost management, and modern platform capabilities. - Hands-on experience with Databricks and modern cloud-based data architectures. - Strong proficiency in advanced SQL and the development and optimization of production-grade data pipelines. - Solid Python experience for automation, data processing, integrations, and engineering workflows. - Experience with dbt or equivalent tools for data transformation and modeling. - Knowledge of CI/CD, source control, automated deployments, and modern software engineering practices. - Strong understanding of Data Governance, Data Catalog, Data Quality, lineage, observability, metadata management, and related practices. - Experience working with Azure or comparable cloud platforms and services. - Practical AI Engineering experience, including consuming AI models through APIs, implementing RAG solutions, developing AI agents, and integrating AI capabilities with enterprise data platforms. - Familiarity with advanced Snowflake capabilities such as Clustering, Materialized Views, Time Travel, Zero-Copy Cloning, Resource Monitoring, and Data Sharing. - Experience with Databricks technologies such as Spark, Delta Lake, Unity Catalog, Workflows, and Asset Bundles is highly desirable. - Knowledge of Azure Data Factory, Data Lake Gen2, Azure Functions, and cloud integration services is a plus. - Familiarity with GitHub, Azure DevOps, DevSecOps practices, and automated delivery workflows is beneficial. - Exposure to AI Engineering frameworks, LLMOps, and Agentic AI technologies is strongly valued. - Strong communication, collaboration, problem-solving, and technical leadership skills, with the ability to work effectively across engineering, analytics, and business teams. ### Benefits - Fully remote work opportunity in Brazil. - Opportunity to work at the intersection of modern data engineering and AI Engineering. - Technical ownership of strategic data, cloud, generative AI, and agentic AI initiatives. - Exposure to technologies including Snowflake, Databricks, Azure, RAG, LLMs, and AI agents. - Opportunity to influence engineering standards, governance, automation, security, and platform scalability. - Collaboration with multidisciplinary technology, analytics, and business teams. - Opportunity to mentor other engineers and contribute to the evolution of data engineering capabilities. - Professional growth in a rapidly evolving data and AI environment. - Competitive compensation and employee benefits, according to the applicable employment structure and local policies.