Middle Data Engineer + AI experience

Requirements

• 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

Responsibilities

• 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

What we offer

• Direct cooperation with the already successful, long-term, and growing project.
• Flexible work arrangements.
• Collaborative and supportive team culture.
• Truly competitive salary.
• Help and support from our caring HR team.

Requirements3+ 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 transform...

Requirements

• 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

Responsibilities

• 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

What we offer

• Direct cooperation with the already successful, long-term, and growing project.
• Flexible work arrangements.
• Collaborative and supportive team culture.
• Truly competitive salary.
• Help and support from our caring HR team.

Requirements3+ 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 transform...