platform engineer for AI data workflows
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Summary
Platform engineer at DeepL building and evolving the cloud data platform — Databricks lakehouse, Kafka ingestion, dlt tooling, and AI-powered data workflows — while owning IaC, CI/CD, observability, and security. Core stack: Python, Terraform/Terragrunt, Docker/Kubernetes; hybrid work from the London office.
Описание
DeepL is a global AI product and research company focused on secure, intelligent solutions for complex business problems. Its Language AI platform provides human-like translation, improved writing, and real-time voice translation for individuals and businesses worldwide.
Задачи
- Build and evolve data platform infrastructure, including the Databricks-based lakehouse, Kafka consumers, and foundational tooling for data engineers
- Support and extend dlt to make data ingestion patterns reusable and robust
- Make technical decisions that enable the platform to scale with DeepL’s data volume and use-case diversity
- Build connectors, interfaces, and integrations that bring data to humans and AI agents, including MCP connectors and workflow skills
- Design AI-powered data workflows for data engineers, analysts, business teams, and AI tools
- Implement data observability, quality frameworks, monitoring, and alerting
- Own infrastructure-as-code, CI/CD for data workflows, access management, security configurations, audit trails, and spend governance
- Build golden-path templates and patterns that help engineers across the company use data safely and efficiently
Требования
- Hands-on experience building and operating cloud-based data infrastructure
- Experience with Terraform/Terragrunt, CI/CD for data workflows, and Docker/Kubernetes
- Production-quality Python proficiency
- Reliability mindset, including observability, meaningful alerts, production ownership, and incident-driven improvements
- Platform-product mindset focused on developer experience and reducing friction for platform users
- Communicates effectively across different audiences and actively uses feedback from data consumers
- Actively uses AI-powered tools to move faster and focus on architecture, system design, and scalability tradeoffs
- Nice to have: experience with Databricks, Apache Iceberg, Kafka, or similar lakehouse and streaming technologies
Условия
- Hybrid work schedule with office attendance twice a week and flexible working hours
- Virtual Shares for every employee
- Regular in-person team events
- Monthly full-day hacking sessions
- 30 Days of annual leave excluding public holidays
- Mental health resources
- Competitive benefits tailored to the employee’s location