MarTech Engineer - AI & Automation
New
J
JobgetherMarketing Technology
Based in GermanyFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 2–3 years
- Required Skills
- Node.jsPythonRESTful APIsHubSpot
Requirements
- 2–3 years of experience in MarTech, marketing automation, technical growth, or a closely related engineering role.
- Strong experience with CRM customization, marketing automation, and workflow design.
- Practical knowledge of APIs, webhooks, event tracking, analytics, and data pipelines.
- Experience with tools such as Customer.io, HubSpot, ActiveCampaign, Make, Zapier, Pipedream, Segment, RudderStack, PostHog, Amplitude, or Mixpanel.
- Familiarity with GA4, Google Tag Manager, Looker Studio, Webflow, Unbounce, HTML, CSS, and JavaScript.
- Ability to write automation and integration logic in Python or Node.js.
- Strong interest in or hands-on experience with AI tools, agent architectures, prompt engineering, or LLM APIs such as OpenAI, Claude, or Gemini.
- Knowledge of AI agent frameworks, vector databases, memory systems, context injection, or lightweight agent orchestration is a plus.
- Understanding of REST APIs, data integration patterns, Git, and technical workflow development.
- Strong problem-solving and analytical skills, with a mindset focused on optimization, scalability, experimentation, and measurable outcomes.
Responsibilities
- Architect and implement AI-enhanced marketing automation workflows using platforms such as Customer.io, HubSpot, Make, Zapier, or similar tools.
- Integrate AI models and agents into lifecycle marketing activities, including lead capture, onboarding, messaging, campaign execution, reporting, and churn prediction.
- Prototype and deploy lightweight AI agents using frameworks such as LangChain, CrewAI, AutoGen, or comparable technologies.
- Design data flows, context management, and integrations that enable AI agents to work effectively with CRM, analytics, support, and campaign data.
- Develop dashboards and reporting that enable rapid experimentation and data-driven decision-making.
- Build internal tools and automation scripts using Python or Node.js to reduce manual work and improve go-to-market efficiency.
- Partner with Product, Engineering, and Growth to establish appropriate triggers, access, feedback loops, and infrastructure for AI agents and automation.
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