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

United States Digital Space LLC; United States; Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work

Pension plans

Health offers

Employee discounts

Job summary

United States Digital Space LLC is seeking a product-minded fullstack engineer with a strong data focus to transform messy ERP data into validated datasets, APIs, and dashboards. You will own the data pipeline from exploration to production, leveraging AI-native workflows to ship high-quality solutions.

You should have a solid backend and frontend background (Python/TypeScript), strong data modeling skills, and hands-on experience with AWS-like environments.

Qualifications

• Strong fullstack/backend experience, ideally with Python and/or TypeScript.
• Experience turning ambiguous problems into working software and APIs.
• Familiar with AI-native workflows, validation, and production-grade quality.
• Proficient in data modeling, KPIs, and analytics-oriented datasets.

Responsibilities

• Build backend services, APIs, internal tools, and automation around data.
• Ingest, transform, validate, and document ERP/API data and map KPIs to sources.
• Create validated, analysis-ready datasets and clear naming for reporting and analytics.
• Deploy and operate reliable cloud solutions (AWS) with monitoring and cost control.

Skills

Fullstack/backend engineering

Python/TypeScript

AI-native development

Data/Analytics experience

Tools

Claude Code

Codex

GitHub Copilot

Job description

Job descriptionWe 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.

• 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

Svenja Krüßel D-49835 Wietmarschen-Lohne Tel.: [phone] E-Mail: