Forward Deployed Engineer Integrations & Customer Success (fmd)

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 were 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. Were a small fast team and every hire shapes the trajectory of the company. Youll 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.

Were just getting started want to build it with us

Tasks

Youll own the reliability and growth of our data infrastructure end-to-end. This isnt a ticket-execution role youll 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 dont break production.
• Push the limits of whats 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 isnt 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; its 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 youre 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 dont just use AI tools we delegate entire workflows to them. Youre 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: Youll 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 Youll Get

• Permanent full-time contract (no B2B)
• Competitive salary ()
• 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!