Forward Deployed Engineer - Integrations & Customer Success

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., * 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., * 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

• 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)