AI Data Engineer
Content + Source + Freshness • 16 Dec 2025 • 95% confidence
Offer value
High value due to focus on innovative AI workflows, collaborative environment, and use of modern technologies, but may encounter limitations in project scope and company size.
- Innovative role focused on AI workflow design
- Collaborative work environment with supportive teamwork
- Engagement with advanced AI technologies
Pros
- Engagement with cutting-edge AI tools and workflows
- Strong emphasis on team collaboration and support
- Opportunities to develop innovative workflows
Cons
- Potential limits on project scope and resources
- Dependence on client requirements may vary
- Uncertain salary specifics
Who it's for
Mid-level to Senior • Remote / Contractor
Good fit
- Mid-level data engineers
- Collaborative team players
- Professionals interested in AI solutions
Not recommended for
- Entry-level candidates without AI experience
- Those seeking rigid project definitions
- Individuals uninterested in automation and AI tools
Motivation fit
Key skills
About the job
• Build and maintain data pipelines (Python, SQL, ETL, APIs) to prepare structured and unstructured data for AI workflows.
• Translate client problems into orchestrated AI workflows, balancing automation and human-in-the-loop design.
• Configure multi-agent logic (planner/worker, feedback loops) using LangChain, Wippy, n8n, Zapier, Make, or custom Python code.
• Prototype and ship proof-of-concepts: onboarding bots, quoting assistants, presales flows, project management helpers.
• Facilitate scoping workshops with stakeholders to clarify requirements and design workflows.
• Collaborate with engineers and product leads to create reusable AI workflow templates and automation patterns.
Requirements
- Strong data engineering experience: Python + SQL, pipelines, ETL, API integrations.
- Experience working with data ahead of AI: cleaning, structuring, connecting multiple sources.
- Hands-on with AI tools: ChatGPT, Claude, LangChain, n8n, Zapier, Make, Autogen, or similar.
- Understanding of LLM orchestration beyond prompt engineering.
- Systems thinking: ability to design workflows with multiple agents, branching logic, loops, and state.
- Strong communication skills: able to explain AI/data concepts and lead workshops with technical and non-technical stakeholders.
- Experience with agent-based frameworks (LangGraph, CrewAI, AutoGen).
- Designed human-in-the-loop workflows (customer support, onboarding, quoting, project management).
- Prototyping with low-code/no-code platforms (Zapier, Airtable, Streamlit, custom GPTs).
- Familiarity with cloud platforms (AWS, GCP, Azure).
- Exposure to ML practices (fine-tuning, RAG, evaluation, multimodal inputs).
🔍 ATS Optimization Keywords
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Hard Skills
- Python
- SQL
- ETL
- APIs
- data pipelines
- AI workflows
- data cleaning
- data structuring
- agent-based frameworks
- ML practices
Soft Skills
- communication skills
- systems thinking
- workshop facilitation
- collaboration
- problem-solving
