Data Engineer, AI – Cloud

• Design, build, and maintain production data pipelines, data models, and reusable data products supporting analytics, enterprise applications, and AI solutions
• Develop and optimize ETL/ELT processes integrating structured and unstructured data from databases, APIs, files, and other enterprise sources
• Implement data quality, testing, monitoring, lineage, and security practices
• Prepare enterprise data for AI use cases, including RAG, retrieval systems, semantic context, and evaluation datasets
• Manage and improve cloud-based data solutions for scalability, performance, reliability, and cost optimization
• Collaborate with AI Engineers, Software Engineers, and Architects on reliable data integrations and reusable engineering solutions
• Work with clients to understand business needs, address data challenges, and deliver solutions aligned with project goals

• Hands-on experience designing, developing, and maintaining production data pipelines, data models, and ETL/ELT processes
• Experience working with cloud data platforms and building scalable data solutions
• Experience implementing data quality, testing, monitoring, and security practices in production environments
• Understanding of data engineering best practices, including performance, reliability, maintainability, and version control
• Strong communication skills, with the ability to explain technical ideas clearly, collaborate with stakeholders, and build relationships with clients and internal teams
• Strong SQL skills, including complex queries, data modeling, transformations, and validation
• Proficiency in Python or another programming language used for production data engineering
• Hands-on experience with at least one major cloud data platform, such as Databricks, Snowflake, Microsoft Fabric, BigQuery, or Redshift
• Experience with data processing, orchestration, and transformation technologies such as Spark, dbt, Airflow, or comparable tools
• Knowledge of data ingestion, APIs, batch processing, and data integration patterns
• Experience preparing structured and unstructured data for AI/GenAI applications, including RAG, embeddings, retrieval, and evaluation datasets is nice to have
• Experience with vector databases, hybrid search, and maintaining retrieval indexes for AI applications is nice to have
• Experience with streaming, change data capture (CDC), or event-driven data architectures is nice to have
• Familiarity with semantic layers, knowledge graphs, feature stores, or MLOps integrations is nice to have
• Experience with CI/CD, infrastructure automation, rapid prototyping, or BI dashboards is nice to have
• Experience working directly with clients in consulting or professional services environments is nice to have

• 100% remote work
• International and multicultural projects
• Opportunities to work with leading technologies in Data, Cloud, and AI ecosystems
• Access to training programs and continuous professional development
• Professional growth opportunities and internal mobility
• A culture built on collaboration, innovation, and continuous learning