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Salary: EUR 90,000 - 110,000 / annual
VOIDS is an AI-powered inventory management platform for mid-size Shopify brands. The company forecasts demand at the product level, prevents stockouts, and automates procurement decisions. Founded in June 2023, VOIDS has achieved 300% growth, €2M ARR, and serves 50+ brands including Hyrox and 6pm. The company is targeting €10M ARR by 2027.
You will own the reliability and growth of the data infrastructure end-to-end as a Forward Deployed Engineer. This is not a ticket-execution role—you will identify problems, design solutions, and ship them yourself.
Key responsibilities include:
**Connectivity Expansion & Integrations**: Expand the data connector ecosystem beyond Shopify and Amazon. Evaluate, implement, and maintain new data sources while preserving system stability. Work directly with customers to understand their data sources, requirements, and edge cases as the first technical contact for data ingestion.
**Customer & Team Collaboration**: Communicate fluently in German and English with customers during onboarding and pilots, and asynchronously with the internal team. Act as a bridge between customer needs and technical implementation, translating real-world data complexity into clean, reliable pipelines. Develop intuitive understanding of e-commerce to proactively suggest solutions.
**Data Pipeline Architecture**: Own the Bronze → Silver → Gold medallion architecture, ensuring logic between layers is airtight, well-documented, and consistent. Scale pipelines to handle more data, faster processing, and lower costs. Find abstraction layers that enable scaling across multiple customers with unique requirements. Improve developer experience for fast iteration cycles.
**AI-Delegated Development Workflows**: Fully embrace AI tooling as a core part of your workflow, delegating end-to-end workflows (testing, development, staging, production) to AI agents where possible. Build and maintain AI-driven pipelines robust enough to handle deep customization without system failures. Combine engineering judgment with AI automation to 10x your output annually.
**Data Quality, Testing & Reliability**: Own the full development lifecycle from testing through production with automated checks at every layer. Set up and maintain robust testing environments and DataOps/MLOps workflows. Proactively identify bottlenecks, inconsistencies, and schema drift before they reach downstream consumers.
The team is small and fast-moving. You will work directly with founders Jannik and Tobias, who have deep e-commerce and AI expertise. The role offers high autonomy, real data scale at 1B+ data points, and work that ships immediately.
Tech stack: Python (Pandas, Polars), SQL, PostgreSQL, AWS S3 (Parquet), BigQuery, Airflow, EventBridge, Docker, Kubernetes, Terraform, Airbyte, AWS SageMaker, AWS Lambda, MLflow, Claude Code, CursorAI.
Work style is AI-first with short daily stand-ups (15 min), efficient weekly planning (30 min), autonomous decision-making, and daily shipping. The role includes 50/50 hybrid flexibility with office in Hamburg city centre.
**REQUIREMENTS**
Must-Have:
- Fluent German and English (written and spoken; customer-facing communication required)
- 3+ years of experience in Data Engineering or closely related roles
- 3+ years of experience in Python, particularly with data manipulation libraries (Pandas, Polars)
- 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
- 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 strong grasp of layered data architectures
- Strong understanding of medallion/layered data architecture and ability to fix one that isn't working properly
- Daily, fluent use of AI tools—actively delegate end-to-end workflows to AI from testing and development through staging and production
- Strong product intuition and understanding with 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 e-commerce data sets and solutions (Shopify, Amazon Seller Central, Google Ads, Meta Ads, Klaviyo, Channable)
- Familiarity with scalable big data tools and frameworks (dbt, dask, Apache Spark, EMR, Databricks, AWS Glue)
- Familiarity or interest in Data Science workflows, especially time series forecasting (Nixtla, Darts, statsmodels, sktime)
- Contributions to developer experience, data observability, or internal tooling improvements
