onhires - Lead Data Engineer Remote | Europe (Berlin-based team)
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Requirements
• 4+ years of experience in Data Engineering, including ownership of production systems • Strong experience building and maintaining reliable ETL pipelines in production • Advanced knowledge of SQL and data modeling principles • Experience with modern ETL and orchestration tools • Strong programming skills (Python or similar) • Solid understanding of data quality, validation, and monitoring • Experience working with large, imperfect datasets and real-world constraints • Experience with DevOps, Docker, and Linux • Familiarity with Agile environments and cross-functional collaboration • Experience in automotive or mobility-related domains • WHAT OUR CLIENT OFFERS • Ownership: Real responsibility over data architecture and quality • Impact: Direct influence on product, customers, and business decisions • Flexibility: Remote-first setup with flexible working conditions • Stability: Established product and a strong, experienced team • Growth: Long-term development and learning opportunities • Team: Collaborative, international environment with high engineering standards
Responsibilities
• Design, build, and maintain scalable, production-grade ETL pipelines • Own and continuously improve data quality frameworks, validation rules, and monitoring • Ensure reliability of data pipelines (handling failures, inconsistencies, late data) • Deliver clear, actionable insights for both technical and non-technical stakeholders • Build and maintain dashboards, reporting systems, and alerting mechanisms • Investigate and resolve data issues with a focus on root cause and long-term stability • Evaluate and improve the data architecture and technology stack • Contribute to code quality, standards, and best practices within the team • Support DevOps-related processes where needed • Align technical solutions with product goals and customer needs • Ability to make architectural decisions and explain trade-offs clearly • Strong sense of ownership and accountability for data systems • Pragmatic mindset — knowing when to optimize vs. keep things simple • Experience improving or scaling existing data platforms • Confidence working across teams and translating data into business impact • Clear communication and structured thinking
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