Globaldev Group
Information Technology and Services
MiddleDataEngineer+AIexperience
Neural analysis suggests this role is
optimal for Mid candidates.
“Middle Data Engineer + AI experience at Globaldev Group. Skills: Data Engineering, AI, LLM, Python, Airflow, dbt, Redshift. 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”
Industry & Context.
Self-directed and proactive: able to spot workflow inefficiencies and drive improvements with minimal supervision
What They're Looking For.
Must Have
3+ years of experience in Data Engineering, 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
What You'll Do.
Analyze business workflows and identify opportunities for data automation and AI-driven process automation
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
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
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
and observability practices
How You'll Work.
Team & Collaboration
Collaborate with business teams, propose solution approaches, build quick POCs, iterate on feedback, and support production deployment; Collaborate with business and technical teams to refine requirements and iterate on solutions
Communication Scope
Excellent communication skills — able to explain technical decisions to non-technical stakeholders
Process & Methodology
Product thinking, build quick POCs, iterate on feedback, support production deployment
Full Job Description
### 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
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