Senior Data Engineer (f/m/d)

<p><strong>We maximize product availability with minimal cashflow investment in 1/10 of the time. We solve a real problem for SMEs. With AI.</strong></p> <p><em>The problem we solve:</em> 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.</p> <p><em>What VOIDS does:</em> 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.</p> <p>The result: <strong>98% inventory efficiency. 20x ROI. Six-figure cash unlocked. Within weeks.</strong></p> <p><em>Traction:</em> Launched June 2023. Since then: 300% growth, 1B+ data points processed, €2M ARR, 50+ brands live — including Hyrox, 6pm, Creamyfabrics, and NatureHeart. Now targeting <strong>€10M ARR by 2027.</strong></p> <p><em>Where we're going:</em> 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.</p> <p><em>Why join now:</em> 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.</p> <p>With high autonomy. At real data scale. With real impact.</p> <h2>Tasks</h2> <p>As a Senior Data Engineer, you'll own the backbone of our product: scalable, robust data pipelines that power our web app, machine learning models, and optimization algorithms. Your role is pivotal—every aspect of VOIDS depends on clean, organized, and reliable data.</p> <p>Our data pipelines already handle over €1,000,000,000 in yearly revenue flowing through customer transactions. Your mission is to build and continuously refine efficient data workflows that seamlessly support complex analytics, accurate forecasting, and agile product development. You'll thrive in complexity, transforming massive transactional datasets into actionable insights for diverse e-commerce brands. Specifically, you will:</p> <ul> <li>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.</li> <li>Ensure data reliability, cleanliness, and timeliness, proactively identifying and addressing bottlenecks or inconsistencies.</li> <li>Deeply understand the product, customer problems, and data specifics to proactively identify, anticipate, and resolve data-related issues.</li> <li>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.</li> <li>Collaborate closely with the CTO, data scientists, software engineers, and customer success teams to translate business requirements into robust data solutions.</li> <li>Continuously improve our data infrastructure, optimize workflows, and advocate for best practices across the engineering and data science teams.</li> <li>Actually get things done, deciding yourself what to focus on—without bureaucracy.</li> </ul> <h2>Requirements</h2> <p><strong>✅ Must-Have Skills</strong></p> <ul> <li>Fluent <strong>English (German</strong> is a plus)</li> <li>Clear, <strong>professional</strong>, asynchronous <strong>communication</strong> abilities</li> <li><strong>3+ years</strong> of experience in Data Engineering or related roles</li> <li>Proven <strong>3+ years</strong> experience in Python, particularly with data manipulation libraries (Pandas, Polars) for efficient data processing</li> <li>Strong proficiency in reading, writing, and updating data in both structured (<strong>SQL databases, especially PostgreSQL</strong>) and unstructured (<strong>AWS S3, Parquet</strong>) storage solutions</li> <li>Hands-on experience building and maintaining scalable <strong>batch data pipelines and workflows</strong> as inputs for web applications and AI models (AWS Lambda, Airflow, MLflow, AWS SageMaker)</li> <li>Proven ability to set up and maintain <strong>robust testing environments</strong>, and manage efficient <strong>DataOps/MLOps workflows</strong> to enable <strong>rapid iteration</strong></li> <li>Familiarity with infrastructure and containerization frameworks (<strong>Kubernetes, Docker, Terraform</strong>)</li> <li>Solid expertise in data storage solutions like <strong>AWS S3 (Parquet)</strong></li> <li>Ability to design and <strong>implement</strong> clean, reliable, and <strong>efficient data processing pipelines</strong> and APIs</li> <li>Strong <strong>product intuition and understanding</strong> with a proactive, <strong>ownership-oriented</strong> <strong>mindset</strong></li> <li><strong>Comfort</strong> with ambiguity and <strong>autonomy</strong> in problem-solving</li> <li><strong>Daily use of AI tools</strong> to enhance productivity, development speed, and problem-solving.</li> </ul> <p><strong>🌟 Bonus / Nice-to-Have</strong></p> <ul> <li>Experience in B2B SaaS startups / scaleups</li> <li>Experience with eCommerce data sets and solutions (Shopify, Amazon Seller Central, Google Ads, Meta Ads, Klaviyo, Channable, etc.)</li> <li>Familiarity with <strong>scalable big data tools and frameworks</strong> (dbt, dask, Apache Spark, EMR, Databricks, AWS Glue)</li> <li>Familiarity or interest in Data Science workflows, especially related to <strong>time series forecasting</strong> (Nixtla, Darts, statsmodels, sktime)</li> <li>Experience with streaming data pipelines</li> <li>Contributions to developer experience, data observability, or internal tooling improvements</li> </ul> <p><strong>🧱 Tech Stack</strong></p> <ul> <li><strong>Programming</strong>: Python (Pandas, Polars), SQL</li> <li><strong>Processing:</strong> AWS SageMaker, AWS Lambda</li> <li><strong>Data Storage &#x26; Management:</strong> PostgreSQL, AWS S3 (Parquet), BigQuery</li> <li><strong>ML Infrastructure</strong>: AWS SageMaker, AWS Lambda, MLflow</li> <li><strong>Orchestration</strong>: Airflow on AWS</li> <li><strong>Collaboration &#x26; AI Tools</strong>: GitHub Copilot, ChatGPT</li> <li><strong>Containerization</strong>: Kubernetes (Airbyte hosting, for data sourcing)</li> </ul> <p><em>Optional, if you're interested in expanding into data science tasks (full-stack mindset appreciated):</em></p> <ul> <li><strong>Modeling &#x26; Analytics:</strong> Statistical, ML, and neural time series forecasting (Nixtla, statsmodels, XGBoost)</li> </ul> <h2>Benefits</h2> <p><strong>🎁 What You’ll Get</strong></p> <ul> <li><strong>Permanent full-time</strong> contract (no B2B)</li> <li><strong>Competitive salary</strong> (€80,000–€100,000) + <strong>Equity</strong></li> <li><strong>30 days</strong> paid vacation</li> <li><strong>All AI subscriptions</strong> with unlimited usage you want</li> <li>New Mac Book Pro &#x26; min. 2 Monitors in the office ;)</li> <li>Regular team events and quarterly off-sites</li> <li>Real ownership and influence</li> <li>A calm, focused work environment that rewards initiative</li> <li><strong>Wellpass membership</strong> to unlimited fitness, yoga, swimming, climbing, and more</li> </ul> <p>We care less about titles and more about impact.</p> <p>When you apply, tell us:</p> <ul> <li>A broken thing you <strong>fixed</strong> — technically or organizationally</li> <li>A feature you shipped that <strong>delighted</strong> users and its financial impact for your company</li> <li>An internal tool or DX improvement <strong>you pushed</strong></li> <li>What <strong>motivates you</strong>, and the kinds of product problems you enjoy</li> </ul> <p>Find more <a href="https://www.arbeitnow.com/english-speaking-jobs">English Speaking Jobs in Germany</a> on Arbeitnow</a>