About the role
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 2027.
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: