We maximize product availability with minimal cashflow investment in 1/10 of the time. We solve a real problem for SMEs. With AI.
The problem we solve: Mid-size Shopify brands lose revenue and cash to stockouts and inefficiencies every day. They can see the problem. They can't fix it fast enough.
What VOIDS does: VOIDS is the AI brain for mid-size Shopify brands. We forecast demand at the product level, catch stockouts and inefficiencies before they happen, and tell e-commerce teams exactly what to do — or execute it automatically with a click.
The result: 98% inventory efficiency. 20x ROI. Six-figure cash unlocked. Within weeks.
Traction: Launched June 2023. Since then: 300% growth, 1B+ data points processed, €2M ARR, 50+ brands live — including Hyrox, 6pm, Creamyfabrics, and NatureHeart. Now targeting €10M ARR by 2027.
Where we're going: Today we own demand forecasting and stock management. Tomorrow: fully autonomous AI-driven procurement. We're not building features — we're rebuilding how modern commerce operates.
Why join now: We're a small, fast team where every hire shapes the company's trajectory. You'll work directly with Jannik and Tobias - two founders who live and breathe e-commerce and AI - and own how we ingest, process, and activate 1B+ data points across our platform. This isn't a maintenance role. You'll build the data foundation for a fully AI-driven future.
With high autonomy. At real data scale. With real impact.
Responsibilities
- Efficiently source, process, and structure diverse datasets—such as transactional, behavioral, product, and marketing data—into clean, actionable formats for our AI models, optimization algorithms, and software engineering teams.
- Ensure data reliability, cleanliness, and timeliness, proactively identifying and addressing bottlenecks or inconsistencies.
- Deeply understand the product, customer problems, and data specifics to proactively identify, anticipate, and resolve data-related issues.
- Act as the first point of action for new data needs, rapidly delivering solutions that enable the rest of the team to iterate fast and independently.
- Collaborate closely with the CTO, data scientists, software engineers, and customer success teams to translate business requirements into robust data solutions.
- Continuously improve our data infrastructure, optimize workflows, and advocate for best practices across the engineering and data science teams.
- Actually get things done, deciding yourself what to focus on—without bureaucracy.
Requirements
- Fluent English (German is a plus)
- Clear, professional, asynchronous communication abilities
- 3+ years of experience in Data Engineering or related roles
- Proven 3+ years experience in Python, particularly with data manipulation libraries (Pandas, Polars) for efficient data processing
- Strong proficiency in reading, writing, and updating data in both structured (SQL databases, especially PostgreSQL) and unstructured (AWS S3, Parquet) storage solutions
- Hands‑on experience building and maintaining scalable batch data pipelines and workflows as inputs for web applications and AI models (AWS Lambda, Airflow, MLflow, AWS SageMaker)
- 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)
- Solid expertise in data storage solutions like AWS S3 (Parquet)
- Ability to design and implement clean, reliable, and efficient data processing pipelines and APIs
- Strong product intuition and understanding with a proactive, ownership-oriented mindset
- Comfort with ambiguity and autonomy in problem-solving
- Daily use of AI tools to enhance productivity, development speed, and problem-solving.
Bonus / Nice‑to‑Have
- Experience in B2B SaaS startups / scaleups
- 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)
- Experience with streaming data pipelines
- Contributions to developer experience, data observability, or internal tooling improvements
Tech Stack
- Programming: Python (Pandas, Polars), SQL
- Processing: AWS SageMaker, AWS Lambda
- Data Storage & Management: PostgreSQL, AWS S3 (Parquet), BigQuery
- ML Infrastructure: AWS SageMaker, AWS Lambda, MLflow
- Orchestration: Airflow on AWS
- Collaboration & AI Tools: GitHub Copilot, ChatGPT
- Containerization: Kubernetes (Airbyte hosting, for data sourcing)
Optional Data Science Tasks
- Modeling & Analytics: Statistical, ML, and neural time series forecasting (Nixtla, statsmodels, XGBoost)
Benefits
- Permanent full‑time contract (no B2B)
- Competitive salary (€80,000–€100,000) + Equity
- 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
Application Prompt
- A broken thing you fixed — technically or organizationally
- A feature you shipped that delighted users and its financial impact for your company
- An internal tool or DX improvement you pushed
- What motivates you, and the kinds of product problems you enjoy