Lead Data Platform Engineer
New
Based in GermanyFull-TimeLead
Salary not disclosed
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Job Details
- Languages
- German (C1 level), English
- Experience
- Minimum of 5 years
- Required Skills
- SQLGCPData engineeringGoCI/CDTerraformData modelingdbt
Requirements
- Minimum of 5 years of experience in Data Engineering, Data Platform Engineering, Platform Engineering, or related roles within production environments.
- Strong proficiency in Go (Golang) with hands-on experience developing and maintaining backend systems and data processing applications.
- Extensive experience with Google Cloud Platform services, including Cloud Run, Pub/Sub, BigQuery, Dataflow, Cloud Storage, and Cloud SQL.
- Advanced SQL skills and solid expertise in analytical data modeling and large-scale data processing architectures.
- Experience with infrastructure-as-code practices using Terraform, CI/CD pipelines, containerized workloads, and modern cloud deployment methodologies.
- Familiarity with SQLMesh, dbt, or similar data orchestration and transformation frameworks is highly desirable.
- Knowledge of Protobuf or comparable schema definition and data serialization technologies.
- Experience implementing monitoring, observability, reliability, and operational best practices in production systems.
- Understanding of web analytics, audience measurement systems, or comparable large-scale data environments is advantageous.
- Practical experience using AI coding assistants and the ability to evaluate AI-generated code for quality, security, and maintainability.
- Excellent communication and stakeholder management skills, with the ability to engage both technical and non-technical audiences.
- Fluent German (C1 level) and good English communication skills for documentation and international collaboration.
- Strong problem-solving abilities, ownership mindset, and a proactive approach to technical leadership.
Responsibilities
- Own the end-to-end data platform roadmap, driving strategic decisions and execution across architecture, operations, and continuous improvement initiatives.
- Take responsibility for the complete data lifecycle, including data ingestion, streaming and batch processing, data modeling, quality assurance, reporting, and delivery pipelines.
- Lead the ongoing optimization of a cloud-native platform, focusing on reliability, scalability, maintainability, operational efficiency, and cost management.
- Establish and strengthen monitoring, observability, alerting, and incident response processes to ensure business-critical systems remain highly available and performant.
- Conduct platform assessments, identify technical risks and improvement opportunities, and develop pragmatic roadmaps for platform evolution.
- Define and promote engineering standards covering documentation, testing, code reviews, infrastructure management, and operational excellence.
- Collaborate closely with product, customer-facing, and leadership teams to translate business objectives into scalable technical solutions.
- Drive the adoption of AI-assisted engineering practices, including coding, testing, documentation, refactoring, and incident analysis workflows.
- Mentor team members, provide technical leadership, and ensure sustainable ownership of critical platform components and processes.
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