Senior Data Engineer (f/m/d)

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

Tasks

As a Senior Data Engineer, you'll own the backbone of our product: scalable, robust data pipelines that power our web app, machine learning models, and optimization algorithms. Your role is pivotal—every aspect of VOIDS depends on clean, organized, and reliable data.

Our data pipelines already handle over €1,000,000,000 in yearly revenue flowing through customer transactions. Your mission is to build and continuously refine efficient data workflows that seamlessly support complex analytics, accurate forecasting, and agile product development. You'll thrive in complexity, transforming massive transactional datasets into actionable insights for diverse e-commerce brands. Specifically, you will:

• 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

✅ Must-Have Skills

• 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 PostgreSQL) 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 SageMaker)

• 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)

• 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</str