Middle Data Engineer + AI experience

Globaldev Group is a team of professionals specializing in creating engineering teams for technological businesses for Western Europe, Israel, and the USA. Founded in 2010 in Kharkiv, with an office opened in Berlin in 2017, the company currently has 220 employees and development hubs in Israel, Ukraine, Portugal, and Poland.

• 3+ years of experience in Data Engineering
• Strong data engineering background with the ability to design and own solutions end-to-end
• Proficiency in Python, Airflow, dbt, and Redshift for data processing, pipeline development, and transformation
• Experience building and maintaining ETL / ELT pipelines and data integrations, including fetching and normalizing data from non-robust 3rd party sources
• Hands-on experience with LLM/agent-based automation applied to business processes (e.g., building agents or LLM-powered workflows for data transformation, testing, or extraction)
• Practical familiarity with modern AI tooling — LLM APIs (OpenAI, Anthropic, etc.), RAG patterns, prompt engineering, and agent frameworks (LangChain, LlamaIndex, or similar)
• Cross-functional flexibility: comfortable stepping beyond pure DE work into adjacent areas — light DevOps (Docker, CI/CD, cloud deployment), backend integration, and basic frontend when a POC requires it
• Excellent communication skills — able to explain technical decisions to non-technical stakeholders
• Self-directed and proactive: able to spot workflow inefficiencies and drive improvements with minimal supervision
• Product thinking: collaborate with business teams, propose solution approaches, build quick POCs, iterate on feedback, and support production deployment

• Analyze business workflows and identify opportunities for data automation and AI-driven process automation
• Design, build, and maintain scalable data pipelines and integrations, including ingestion from unreliable or unstructured 3rd party sources
• Build LLM- and agent-based solutions for data transformation, validation/testing, and extraction tasks
• Containerize data and AI workloads using Docker and deploy to cloud infrastructure (AWS)
• Develop prototypes and POCs to validate ideas quickly — both data pipelines and AI-powered workflows
• Collaborate with business and technical teams to refine requirements and iterate on solutions
• Support the deployment and integration of data and AI solutions into production systems
• Continuously improve data processes through automation and AI-driven approaches
• Contribute to data modeling, quality, and observability practices