Forward Deployed Engineer - Integrations & Customer Success (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>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.</p>
<p>The result: 98% inventory efficiency, 20x ROI, and six-figure cash unlocked. Within weeks.</p>
<p>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.</p>
<p>Today we own demand forecasting and stock management. Our vision for tomorrow: AI handles procurement end-to-end — fully autonomous.</p>
<p><strong>This is where you come in.</strong> 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.</p>
<p>High autonomy. Real data scale. Work that actually ships.</p>
<p><strong>We're just getting started — want to build it with us?</strong></p>
<h2>Tasks</h2>
<p>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.</p>
<p><strong>Connectivity Expansion &#x26; Integrations</strong></p>
<ul>
<li>Expand our data connector ecosystem far beyond Shopify and Amazon, paving the way for complete AI-driven custom integrations.</li>
<li>Evaluate, implement, and maintain new data sources in a way that works with existing flows — system stability and customization tolerance are non-negotiable.</li>
<li>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.</li>
</ul>
<p><strong>Customer &#x26; Team Collaboration</strong></p>
<ul>
<li>Communicate fluently in <strong>German and English</strong> — with customers during onboarding and pilot projects, and async with the internal team.</li>
<li>Act as a bridge between customer needs and technical implementation, translating real-world data messiness into clean, reliable pipelines.</li>
<li>Understand the e-commerce space intuitively - Suggest solutions to customers and implemented them before the customers even asks for it.</li>
</ul>
<p><strong>Data Pipeline Architecture</strong></p>
<ul>
<li>Take ownership of our Bronze → Silver → Gold medallion architecture: the logic between layers needs to be airtight, well-documented, and consistent.</li>
<li>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.</li>
<li>Improve Developer Experience: Enable fast iterations cycles and smooth developer experience when working with existing systems or building new things on top.</li>
</ul>
<p><strong>AI-Delegated Development Workflows</strong></p>
<ul>
<li>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.</li>
<li>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.</li>
<li>Push the limits of what's achievable by combining your engineering judgment with AI automation. 10x yourself every year.</li>
</ul>
<p><strong>Data Quality, Testing &#x26; Reliability</strong></p>
<ul>
<li>Own the full development lifecycle: testing → development → staging → production, with automated checks at every layer.</li>
<li>Set up and maintain robust testing environments and DataOps/MLOps workflows to enable rapid iteration.</li>
<li>Proactively identify bottlenecks, inconsistencies, and schema drift — and fix them before they reach downstream consumers.</li>
</ul>
<h2>Requirements</h2>
<p>**<br>
✅ Must-Have Skills**</p>
<ul>
<li><strong>Fluent German and English</strong> — both written and spoken (customer-facing communication required)</li>
<li><strong>3+ years</strong> of experience in Data Engineering or closely related roles</li>
<li><strong>3+ years</strong> experience in Python, particularly with data manipulation libraries (Pandas, Polars) for efficient data processing</li>
<li>Deep proficiency in <strong>SQL and PostgreSQL</strong> for structured data</li>
<li>Hands-on experience building and maintaining scalable <strong>streaming, event-driven and batch data pipelines and workflows</strong> as inputs for web applications and AI models</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>End-to-end expertise in <strong>designing and operating scalable data platforms</strong>, including storage (S3/Parquet), data pipelines, APIs, and connectors, with a strong grasp of layered data architectures.</li>
<li>Strong understanding of <strong>medallion / layered data architecture</strong> — and the ability to fix one that isn't working properly</li>
<li><strong>Daily, fluent use of AI tools</strong> — 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.</li>
<li>Strong <strong>product intuition and understanding</strong> with a proactive, <strong>ownership-oriented</strong> <strong>mindset</strong></li>
<li><strong>Comfortable</strong> with ambiguity, autonomous <strong>decision-making</strong>, and direct <strong>customer contact</strong></li>
</ul>
<p><strong>🌟 Bonus / Nice-to-Have</strong></p>
<ul>
<li>Experience in B2B AI startups / scale-ups</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>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>Data Storage &#x26; Management:</strong> PostgreSQL, AWS S3 (Parquet), BigQuery</li>
<li><strong>Orchestration</strong>: Airflow, EventBridge, Crons..</li>
<li><strong>AI Tools</strong>: Claude Code, CursorAI Agents</li>
<li><strong>Containerization</strong>: Docker, Kubernetes, Terraform</li>
<li><strong>Data Integration:</strong> Airbyte (self-hosted on Kubernetes)</li>
<li><strong>Processing &#x26; ML:</strong> AWS SageMaker, AWS Lambda, MLflow</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>🤖 How We Work</strong></p>
<ul>
<li><strong>AI-first engineering:</strong> 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.</li>
<li><strong>Fast-paced, high-impact, no overhead:</strong> Short daily stand-ups (15min), efficient weekly planning (30min), autonomous decisions, ship daily</li>
<li><strong>Pragmatic engineering values:</strong> simplicity, maintainability, customer focus — no over-engineering.</li>
<li><strong>Customer proximity:</strong> You'll be in direct contact with customers in pilot projects. Good communication matters as much as good code.</li>
<li><strong>50/50 hybrid:</strong> Remote flexibility combined with our office in Hamburg city centre with drinks and snacks.</li>
<li><strong>Autonomous decision making:</strong> We trust engineers to own their work and loop others in when needed, typically there is only <strong>lightweight consultation</strong> with the CTO and engineers</li>
</ul>
<p><strong>🎁 What You’ll Get</strong></p>
<ul>
<li><strong>Permanent full-time</strong> contract (no B2B)</li>
<li><strong>Competitive salary</strong> (€90,000–€110,000)</li>
<li><strong>Equity</strong> available for senior hires</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><strong>🧑‍🏫 Hiring Process</strong></p>
<p>We move fast and keep it simple.</p>
<ul>
<li>Initial Screening (30 min)</li>
<li>Technical Interview with CTO (30 min)</li>
<li>Realistic Live Coding Challenge (90 min)</li>
<li>Meet the Team in Hamburg</li>
<li>Offer within 2 weeks from start to decision</li>
</ul>
<p><strong>💡 How to apply</strong></p>
<p>We care less about titles and more about impact.</p>
<p>When you apply, tell us:</p>
<ul>
<li>A <strong>connector or integration you built</strong> and what complexity you dealt with</li>
<li>How you currently use <strong>AI in your daily engineering workflow</strong> — concretely, not in theory</li>
<li>What <strong>motivates you</strong>, and what kinds of data problems you find genuinely interesting</li>
</ul>
<p><strong>👉</strong> Send us your answers and your CV: <a ----------
<pre><code> Or shoot us a message on LinkedIn!
</code></pre>
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