Data Engineer

Darum lohnt es sich

Collaborate with key stakeholders, such as product teams, data engineers, back-end developers, and ML engineers, to build tools & frameworks that power analytics, product features, and data-driven workflows.

Work with business stakeholders such as Analytics Eng, Data Science, Machine Learning, and Product teams to build high-impact data products enabling business-critical features, research, and experimentation with a focus on efficiency and alignment Collaborate with Leadership in devising the charter and technical roadmaps Mentor engineers across back-end infrastructure and data engineering disciplines, fostering a culture of collaboration and technical excellence.

Hands-on experience with modern data storage technologies (e.g., Delta Lake, Snowflake, BigQuery, Redshift). Is an excellent communicator and collaborator, able to work across diverse teams and stakeholders, translates business needs into robust data solutions, and ensures alignment on technical goals.

Has a demonstrated ability to work independently with minimal to zero guidance, proactively manages tasks and priorities across multiple projects, analyzes and executes work efficiently, collaborates effectively with cross-functional teams, and thrives in fast-paced, results-driven environments.

Position: Data Engineer Relocation to Netherlands or Germany In This Role, You Will Architect and lead the development of large-scale systems for data pipelines, data lakes that handle billions of daily events.

Design and implement solutions that ensure data is available, secure, and scalable across the platform, enabling real-time and batch processing. Make high-level architectural decisions about system design, technology choices, and platform evolution, ensuring scalability and long-term sustainability.

Qualifications Has experience in large-scale distributed computing systems, with expertise in both data engineering and data modeling. Has experience managing live production environments, including high-load systems or data-intensive workflows, with a focus on uptime and reliability.

Expert in SQL and proficient in technologies like Spark, Kafka, Terraform, and in at least one programming language commonly used for data engineering (e.g., Python, Scala, or Java). Strong knowledge of ETL/ELT design patterns, orchestration tools (e.g., Airflow, dbt, Dagster), and data quality frameworks.

Ability to design scalable, secure, and maintainable data models and architectures, and understanding of data governance, privacy regulations. Is a strategic thinker, able to evaluate whether to build internally or leverage third-party solutions, ensuring the right balance between speed, scalability, and long-term viability.