Data Engineer Coordinator – AI Engineering

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Design, develop, and enhance data solutions within contemporary analytics environments.

• Act as a technical reference for strategic projects and promote the adoption of best practices in data engineering.

• Create and implement enterprise agentic solutions, including LLM orchestration, reasoning workflows, external tools, and API integration with company data.

• Develop Retrieval-Augmented Generation (RAG) pipelines, encompassing indexing, retrieval, and context enrichment.

• Improve agents to access knowledge bases and analytical and enterprise systems with proper authentication and access controls.

• Ensure observability, manage inference costs, select appropriate LLMs, and maintain security in cloud applications.

• Create and optimize scalable, secure, and high-performance data pipelines.

• Provide technical leadership for data engineering projects utilizing Snowflake, Databricks, and cloud platforms.

• Establish and implement best practices for development, testing, observability, and automation.

• Contribute to data modeling and the creation of reusable, sustainable analytics solutions.

• Identify opportunities to enhance performance, operational efficiency, and cost optimization.

• Assist in technical reviews and mentor junior engineers.

• Ensure adherence to security, governance, and data quality standards.

• Collaborate with business, analytics, and technology teams to define effective data solutions.

• Aid in the implementation of AI Engineering capabilities by integrating generative AI applications, intelligent agents, and solutions based on enterprise data.

⛳️ Requirements

• Extensive experience in Data Engineering and deploying analytics solutions at scale.

• In-depth knowledge of Snowflake, including data modeling, query optimization, performance tuning, security, and cost management.

• Familiarity with Databricks and modern cloud data architectures.

• Expertise in advanced SQL and data pipeline development.

• Proficient in Python for automation, data processing, and integration tasks.

• Experience with dbt or similar tools for data transformation and modeling.

• Understanding of CI/CD, version control, and deployment automation methodologies.

• Knowledge of Data Governance, Data Catalog, and Data Quality principles.

• Experience in Azure environments or comparable cloud platforms.

• Practical experience in AI Engineering, including utilizing AI models via 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