Company
Technology
LeadDataPlatformEngineer
Neural analysis suggests this role is
optimal for Lead candidates.
“Lead Data Platform Engineer. Skills: Data Platform Engineering, Cloud-native architecture, Data lifecycle management, AI-assisted engineering. Own data platform roadmap. Drive strategic decisions”
Industry & Context.
Problem-solving; Root cause analysis
What They're Looking For.
Must Have
5 years of experience in Data Engineering, 5 years of experience in Data Platform Engineering, 5 years of experience in Platform Engineering, Proficiency in Go (Golang), Extensive experience with Google Cloud Platform services, Advanced SQL skills, Solid expertise in analytical data modeling, Solid expertise in large-scale data processing architectures, Experience with infrastructure-as-code practices, Experience with CI/CD pipelines, Experience with containerized workloads, Experience with modern cloud deployment methodologies, Experience implementing monitoring, Experience implementing observability, Experience implementing reliability, Experience implementing operational best practices, Practical experience using AI coding assistants, Ability to evaluate AI-generated code, Excellent communication skills, Excellent stakeholder management skills, Fluent German (C1 level), Good English communication skills, Problem-solving abilities, Ownership mindset, Proactive approach to technical leadership
Nice to Have
Familiarity with SQLMesh, Familiarity with dbt, Familiarity with similar data transformation frameworks, Knowledge of Protobuf, Knowledge of comparable schema definition technologies, Knowledge of comparable data serialization technologies, Understanding of web analytics, Understanding of audience measurement systems, Understanding of comparable large-scale data environments
What You'll Do.
Own data platform roadmap
Drive strategic decisions
Drive execution across architecture
Drive execution across operations
Drive execution across continuous improvement
Take responsibility for data lifecycle
Manage data ingestion
Manage streaming processing
Manage batch processing
Manage quality assurance
Manage delivery pipelines
Lead optimization of cloud-native platform
Focus on maintainability
Focus on operational efficiency
Focus on cost management
Establish monitoring processes
Establish observability processes
Establish alerting processes
Establish incident response processes
Ensure business-critical systems availability
Ensure business-critical systems performance
Conduct platform assessments
Identify technical risks
Identify improvement opportunities
Develop pragmatic roadmaps
Define engineering standards
Promote engineering standards
Collaborate with product teams
Collaborate with customer-facing teams
Collaborate with leadership teams
Translate business objectives
Drive adoption of AI-assisted engineering practices
Provide technical leadership
Ensure sustainable ownership of platform components
Ensure sustainable ownership of platform processes
How You'll Work.
Team & Collaboration
Collaborate with product teams; Collaborate with customer-facing teams; Collaborate with leadership teams; Cross-functional collaboration
Communication Scope
Technical documentation; International collaboration
Process & Methodology
Roadmap planning
Full Job Description
## Accountabilities 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. 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 Terraf
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