Senior Data Engineer (f/m/d)

We maximize product availability with minimal cashflow investment in 1/10 of the time. We solve a real problem for SMEs. With AI.

The problem we solve: 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.

What VOIDS does: 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.

The result: 98% inventory efficiency. 20x ROI. Six-figure cash unlocked. Within weeks.

Traction: Launched June 2023.

Since then: 300% growth, 1B+ data points processed, €2M ARR, 50+ brands live — including Hyrox, 6pm, Creamyfabrics, and Nature Heart.

Now targeting €10M ARR by 2027.

Where we're going: 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.

Why join now: 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.

With high autonomy. At real data scale. With real impact.

Responsibilities

• 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.
• Ensure data reliability, cleanliness, and timeliness, proactively identifying and addressing bottlenecks or inconsistencies.
• Deeply understand the product, customer problems, and data specifics to proactively identify, anticipate, and resolve data-related issues.
• 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.
• Collaborate closely with the CTO, data scientists, software engineers, and customer success teams to translate business requirements into robust data solutions.
• Continuously improve our data infrastructure, optimize workflows, and advocate for best practices across the engineering and data science teams.
• Actually get things done, deciding yourself what to focus on—without bureaucracy.

Requirements

• Fluent English (German is a plus)
• Clear, professional, asynchronous communication abilities
• 3+ years of experience in Data Engineering or related roles
• Proven 3+ years experience in Python, particularly with data manipulation libraries (Pandas, Polars) for efficient data processing
• Strong proficiency in reading, writing, and updating data in both structured (SQL databases, especially Postgre SQL) and unstructured (AWS S3, Parquet) storage solutions
• Hands‑on experience building and maintaining scalable batch data pipelines and workflows as inputs for web applications and AI models (AWS Lambda, Airflow, MLflow, AWS Sage Maker)
• Proven ability to set up and maintain robust testing environments, and manage efficient Data Ops/MLOps workflows to enable rapid iteration
• Familiarity with infrastructure and containerization frameworks (Kubernetes, Docker, Terraform)
• Solid expertise in data storage solutions like AWS S3 (Parquet)
• Ability to design and implement clean, reliable, and efficient data processing pipelines and APIs
• Strong product intuition and understanding with a proactive, ownership-oriented mindset
• Comfort with ambiguity and autonomy in problem-solving
• Daily use of AI tools to enhance productivity, development speed, and problem-solving.
• Bonus / Nice‑to‑Have
• Experience in B2B Saa S startups / scaleups
• Experience with e Commerce 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)
• Experience with streaming data pipelines
• Contributions to developer experience, data observability, or internal tooling improvements
• Tech Stack
• Programming: Python (Pandas, Polars), SQL
• Processing: AWS Sage Maker, AWS Lambda
• Data Storage & Management: Postgre SQL, AWS S3 (Parquet), Big Query
• ML Infrastructure: AWS Sage Maker, AWS Lambda, MLflow
• Orchestration: Airflow on AWS
• Collaboration & AI Tools: Git Hub Copilot, Chat GPT
• Containerization: Kubernetes (Airbyte hosting, for data sourcing)
• Optional Data Science Tasks
• Modeling & Analytics: Statistical, ML, and neural time series forecasting (Nixtla, statsmodels, XGBoost)

Benefits

• Permanent full‑time contract (no B2B)
• Competitive salary (€80,000–€100,000) + Equity
• 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
• Application Prompt
• A broken thing you fixed — technically or organizationally
• A feature you shipped that delighted users and its financial impact for your company
• An internal tool or DX improvement you pushed
• What motivates you, and the kinds of product problems you enjoy
• #J-18808-Ljbffr