Forward Deployed Engineer - Integrations & Customer Success (f/m/d)

descriptionDescrizione dell’offerta

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: Or shoot us a message on LinkedIn! Find Jobs in Germany on Arbeitnow