Senior AI-Native Fullstack Engineer (mfd) Data & Analytics

We are building a modern analytics and Business Intelligence solution for customers in the temp-staffing industry integrating operational data from multiple ERP systems across countries into reliable customer-facing insights analytical workflows and reusable data products.

This is not a traditional data analyst or classic BI developer role. We are looking for a product-minded fullstack engineer with a strong data focus: someone who can move from messy ERP data and product-defined KPIs to validated datasets pipelines APIs internal tools and dashboards where needed.

AI and LLM tooling are central to how we work. We expect someone who uses AI-native workflows to explore faster build in parallel validate assumptions and ship high-quality production solutions.

What Were Looking For
We are looking for a fullstack engineer with a strong data focus. You should turn ambiguous problems into working software use AI as a default development workflow care about correctness and maintainability understand data edge cases choose simple robust solutions own the outcome from exploration to production and move quickly while verifying aggressively.

AI-Native Development

• Hands-on with Claude Code Codex and agent-based workflows; GitHub Copilot-style autocomplete alone is not enough
• Familiar with worktrees subagents MCP structured prompts harness engineering parallelization and validating AI-generated code and analysis to production quality

Software Engineering

• Strong fullstack/backend experience ideally with Python and/or TypeScript
• Able to build production-grade services APIs scripts tools automation and clean interfaces; comfortable with version control review debugging testing and existing systems

Data Engineering & Analytics

• Strong SQL data modeling analytical schemas transformations and downstream data use
• Able to translate product-defined KPIs into datasets and metrics and validate messy operational data edge cases system limitations and customer-specific differences

Cloud & Infrastructure

• Hands-on with AWS or similar cloud environments including storage databases queues containers serverless/scheduled processing SDKs and APIs
• Understands deployment secrets networking permissions runtime configuration scalability performance cost and operational trade-offs

Good Fit
You may be a good fit if you are a fullstack/backend engineer with strong data or analytics experience a Python/TypeScript engineer who enjoys data products and automation an analytics/data engineer with real software engineering depth a technical founder/builder profile or an AI-native engineer using LLMs and agents daily for production work.

Not a Good Fit
This role is probably not the right fit if you are mainly a dashboard-only BI analyst classic report builder pure data warehouse engineer waiting for predefined tickets notebook-only analyst without production engineering experience engineer with no interest in data modeling someone who avoids ambiguity or someone who does not actively use and rigorously validate AI-generated output.

Svenja Krüßel
D-49835 Wietmarschen-Lohne
Tel.: 0170-7888740
E-Mail:

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