Senior AI-Native Fullstack Engineer (m/f/d) - Data & Analytics

zvoove Group; Germany (OH); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Pension plans

Health offers

Employee discounts

Flexible working models

Job summary

zvoove Group is seeking a fullstack engineer with a strong data focus to drive AI-native analytics and data products across ERP data and KPIs. You will build backend services, data pipelines, and dashboards while embracing AI workflows and production-grade practices.

The role emphasizes hands-on with AI tools, data modeling, and cloud deployments (AWS), delivering reliable, customer-facing analytics and internal tooling.

Qualifications

• Strong fullstack/backend experience with Python and/or TypeScript.
• Experience building production-grade services, APIs, scripts, tools, automation, and data products.
• Strong SQL and data modeling skills for analytics datasets.

Responsibilities

• Fullstack Product Engineering: Build backend services, APIs, internal tools, lightweight UI/admin screens, automation, job runners, integrations, and customer-specific configuration around the data.
• Data Pipeline & Modeling: Ingest, validate, transform, and document ERP, API, SQL, file, and cloud data; map KPIs to sources and identify gaps.
• Curated Data Products: Create validated, analysis-ready datasets with consistent schemas and reproducible transformations.
• Cloud & Production Ownership: Deploy and operate reliable cloud solutions, preferably AWS, with monitoring, alerts, and cost control.

Skills

Fullstack experience

Python

TypeScript

SQL

Data modeling

Analytical schemas

AI-native workflows

Harness engineering

Parallelization

Tools

Claude Code

Codex

Agent-based workflows

GitHub Copilot

Job description

Job Description

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.

What We’re Looking For

We are looking for a 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.

Your tasks

• Fullstack Product Engineering: Build backend services, APIs, internal tools, lightweight UI/admin screens, automation, job runners, integrations, and customer‑specific configuration around the data.
• Data Pipeline & Modeling: Ingest, validate, transform, and document ERP, API, SQL, file, and cloud data; map product‑defined KPIs to available sources and identify gaps or inconsistencies.
• Curated Data Products: Create validated, analysis‑ready datasets with consistent schemas, reproducible transformations, and clear naming for reporting, APIs, product features, and customer‑facing analytics.
• Cloud & Production Ownership: Deploy and operate reliable cloud solutions, preferably AWS, owning monitoring, alerts, failure handling, performance, cost, and operational reliability.

Your profile

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.

Your benefits

• Collaboration in an empathetic, appreciative team with room to contribute ideas and take ownership; individual development opportunities, structured onboarding, and interdisciplinary collaboration.
• Flexible working models including hybrid work, home office, and mobile working.
• A modern tech environment and agile ways of working.
• Additional benefits such as pension plans, health offers, and employee discounts.