Senior AI-Native Fullstack Engineer (m/f/d) - 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 We’re 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: [email protected]