We maximize product availability with minimal cashflow investment in 1/10 of the time. We solve a real problem for SMEs. With AI.
VOIDS is the AI brain for mid-size Shopify brands inventory. We forecast demand at the product level, catch stockouts and inefficiencies before they happen, and give e-commerce teams exactly the right action — or execute it automatically with a single click.
The result: 98% inventory efficiency, 20x ROI, and six-figure cash unlocked. Within weeks.
We launched in June 2023. Since then: 300% growth, 1B+ data points processed, €2M ARR, and 50+ brands live — including Hyrox, 6pm, Creamyfabrics, and NatureHeart. Now, we're targeting €10M ARR by 2028.
Today we own demand forecasting and stock management. Our vision for tomorrow: AI handles procurement end-to-end — fully autonomous.
This is where you come in. We're a small, fast team and every hire shapes the trajectory of the company. You'll shape how we ingest, process, and activate 1B+ data points, and help us build the data foundation for a fully AI-driven procurement future. Work directly with Jannik and Tobias, who live and breathe e-commerce and AI.
High autonomy. Real data scale. Work that actually ships.
We're just getting started — want to build it with us?
Tasks
You'll own the reliability and growth of our data infrastructure end-to-end. This isn't a ticket-execution role — you'll identify problems, design solutions, and ship them yourself.
Connectivity Expansion & Integrations
- Expand our data connector ecosystem far beyond Shopify and Amazon, paving the way for complete AI-driven custom integrations.
- Evaluate, implement, and maintain new data sources in a way that works with existing flows — system stability and customization tolerance are non-negotiable.
- Work closely with customers to understand their data sources, requirements, and edge cases — you are the first technical contact when it comes to what data goes into our system.
Customer & Team Collaboration
- Communicate fluently in German and English — with customers during onboarding and pilot projects, and async with the internal team.
- Act as a bridge between customer needs and technical implementation, translating real-world data messiness into clean, reliable pipelines.
- Understand the e-commerce space intuitively - Suggest solutions to customers and implemented them before the customers even asks for it.
Data Pipeline Architecture
- Take ownership of our Bronze → Silver → Gold medallion architecture: the logic between layers needs to be airtight, well-documented, and consistent.
- Scale the piplines to new heights: More data, faster pipelines, less costs. You need to find abstraction layer that allow to scale across multiple customer with very unique requirements.
- Improve Developer Experience: Enable fast iterations cycles and smooth developer experience when working with existing systems or building new things on top.
AI-Delegated Development Workflows
- Fully embrace AI tooling — not just as a productivity booster, but as a core part of how you work: delegate end-to-end workflows (testing, development, staging, production) to AI agents where possible.
- Build and maintain AI-driven pipelines that can handle deep customiszation without system failures — the architecture must be robust enough that AI-generated changes don't break production.
- Push the limits of what's achievable by combining your engineering judgment with AI automation. 10x yourself every year.
Data Quality, Testing & Reliability
- Own the full development lifecycle: testing → development → staging → production, with automated checks at every layer.
- Set up and maintain robust testing environments and DataOps/MLOps workflows to enable rapid iteration.
- Proactively identify bottlenecks, inconsistencies, and schema drift — and fix them before they reach downstream consumers.
Requirements
**
✅ Must-Have Skills**
- Fluent German and English — both written and spoken (customer-facing communication required)
- 3+ years of experience in Data Engineering or closely related roles
- 3+ years experience in Python, particularly with data manipulation libraries (Pandas, Polars) for efficient data processing
- Deep proficiency in SQL and PostgreSQL for structured data
- Hands-on experience building and maintaining scalable streaming, event-driven and batch data pipelines and workflows as inputs for web applications and AI models
- Proven ability to set up and maintain robust testing environments, and manage efficient DataOps/MLOps workflows to enable rapid iteration
- Familiarity with infrastructure and containerization frameworks (Kubernetes, Docker, Terraform)
- End-to-end expertise in designing and operating scalable data platforms, including storage (S3/Parquet), data pipelines, APIs, and connectors, with a strong grasp of layered data architectures.
- Strong understanding of medallion / layered data architecture — and the ability to fix one that isn't working properly
- Daily, fluent use of AI tools — you actively delegate end-to-end workflows to AI: from testing and development through to staging and production. AI is not a helper tool; it's how you multiply your output.
- Strong product intuition and understanding with a proactive, ownership-oriented mindset
- Comfortable with ambiguity, autonomous decision-making, and direct customer contact
🌟 Bonus / Nice-to-Have
- Experience in B2B AI startups / scale-ups
- Experience with eCommerce data sets and solutions (Shopify, Amazon Seller Central, Google Ads, Meta Ads, Klaviyo, Channable, etc.)
- Familiarity with scalable big data tools and frameworks (dbt, dask, Apache Spark, EMR, Databricks, AWS Glue)
- Familiarity or interest in Data Science workflows, especially related to time series forecasting (Nixtla, Darts, statsmodels, sktime)
- Contributions to developer experience, data observability, or internal tooling improvements
🧱 Tech Stack
- Programming: Python (Pandas, Polars), SQL
- Data Storage & Management: PostgreSQL, AWS S3 (Parquet), BigQuery
- Orchestration: Airflow, EventBridge, Crons..
- AI Tools: Claude Code, CursorAI Agents
- Containerization: Docker, Kubernetes, Terraform
- Data Integration: Airbyte (self-hosted on Kubernetes)
- Processing & ML: AWS SageMaker, AWS Lambda, MLflow
Optional, if you're interested in expanding into data science tasks (full-stack mindset appreciated):
- Modeling & Analytics: Statistical, ML, and neural time series forecasting (Nixtla, statsmodels, XGBoost)
Benefits
🤖 How We Work
- AI-first engineering: We don't just use AI tools — we delegate entire workflows to them. You're expected to embrace this fully and help us push it further.
- Fast-paced, high-impact, no overhead: Short daily stand-ups (15min), efficient weekly planning (30min), autonomous decisions, ship daily
- Pragmatic engineering values: simplicity, maintainability, customer focus — no over-engineering.
- Customer proximity: You'll be in direct contact with customers in pilot projects. Good communication matters as much as good code.
- 50/50 hybrid: Remote flexibility combined with our office in Hamburg city centre with drinks and snacks.
- Autonomous decision making: We trust engineers to own their work and loop others in when needed, typically there is only lightweight consultation with the CTO and engineers
🎁 What You’ll Get
- Permanent full-time contract (no B2B)
- Competitive salary (€90,000–€110,000)
- Equity available for senior hires
- 30 days paid vacation
- All AI subscriptions with unlimited usage you want
- New Mac Book Pro & min. 2 Monitors in the office ;)
- Regular team events and quarterly off-sites
- Real ownership and influence
- A calm, focused work environment that rewards initiative
- Wellpass membership to unlimited fitness, yoga, swimming, climbing, and more
🧑🏫 Hiring Process
We move fast and keep it simple.
- Initial Screening (30 min)
- Technical Interview with CTO (30 min)
- Realistic Live Coding Challenge (90 min)
- Meet the Team in Hamburg
- Offer within 2 weeks from start to decision
💡 How to apply
We care less about titles and more about impact.
When you apply, tell us:
- A connector or integration you built and what complexity you dealt with
- How you currently use AI in your daily engineering workflow — concretely, not in theory
- What motivates you, and what kinds of data problems you find genuinely interesting
👉 Send us your answers and your CV: Or shoot us a message on LinkedIn!
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