Our Tech Stack

We strongly believe in self-hosted open source technologies backed by battle-proven AWS technologies.

• Apache Kafka as our event streaming system

• Apache Flink (using Java) for stateful stream processing and real-time feature engineering

• Go as our primary language for backend

• Kubernetes and Terraform to manage our infrastructure

• S3 and Druid for long-term data storage

• DynamoDB and Redis to store per-user data and power low-latency real-time aggregations.

• TensorFlow and PyTorch for training ML models, TensorFlow Serving and Triton for inference

• Prometheus, Grafana, the ELK stack and OpenObserve for logging and monitoring

• Airflow for orchestration

• … and we’re always open to trying new technologies that suit each case best.

• Scale the Feature Store: Leverage Apache Flink to translate Data Science requirements into high-performance, real-time streams. Enable Data Scientists to contribute new features and implement them yourself when needed.

• Ensure Data Quality, Observability & Integrity: Implement data validation, monitoring, and governance processes to maintain accuracy, consistency, and reliability across all datasets and features in Feature Store.

• Optimize Pipelines Performance: Identify and eliminate bottlenecks in complex ETL jobs, transforming long-running processes into streamlined, rapid-iteration cycles.

• Bridge Data Science & Backend Teams: Act as the key link between data science and backend engineering, ensuring seamless data integration and usage across the organization.

• Explore new Data Sources: Partner with Data Scientists to build custom ingestion logic for unstructured or non-typical data sources, handling the heavy preprocessing needed for experimental research.

• Evolve the Data Architecture: Maintain and optimize our Data Lake. You’ll help us decide on the future of our storage (e.g., moving toward a Data Lakehouse model) and implement best practices.

• Work in an International Environment: Join an international, English-speaking team focused on scaling our adtech platform to new heights.

• You have 5+ years’ of software development experience, working on modern data engineering stack. 

• You have proven experience with Apache Flink for stateful stream processing and real-time feature computation.

• You have worked extensively with real-time data streaming systems such as Kafka, Kinesis, or Pub/Sub.

• You have experience with systems handling several TB of data per day and multiple thousand events per second.

• You know how to identify bottlenecks in data pipelines and you have experience in optimizing them and scaling them up.

• You have strong Java knowledge, Golang/Python knowledge would be a plus.

• You have worked closely with Data Scientists on online ML systems with low latency.

• You know how to move beyond “raw data” to design robust, multi-layered data architectures. You have hands-on experience using dbt to build these layers and can guide us on the best tools and formats to manage them at scale.

• You know scheduling frameworks such as Airflow / Kubeflow. 

• You know the concepts of data quality and how to apply them in production.

• You are familiar with relational and NoSQL databases.

• You are open to relocating to Hamburg, Germany

• You have strong problem-solving skills and ability to tackle complex technical challenges.

• Plus: You have hands-on experience in working with AWS, Terraform and Kubernetes.

• Plus: You are familiar with the Medallion Architecture and have experience building Semantic Layers for downstream data consumption.