The opportunity
DeepL is a global AI product and research company focused on building secure, intelligent solutions to complex business problems. Over 200,000 business customers and millions of individuals across 228 global markets today trust DeepL's Language AI platform for human-like…
What you'll do
Build and evolve the data platform infrastructure: shape and advance the core infrastructure our data ecosystem runs on — our Databricks-based lakehouse, Kafka consumers that reliably ingest data at scale, and the foundational layer that data engineers build their workflows on top of. You'll also support and extend tooling like dlt (data load tool) to make ingestion patterns reusable and robust, and make technical decisions that let the platform grow with DeepL's data volume, use-case diversity, and scale
Enable AI-powered data workflows: build the connectors, interfaces, and integrations that bring data into the hands of humans and AI agents alike, including MCP connectors and workflow skills that let the rest of DeepL access and work with data in AI-assisted workflows. Design for the full range of users — data engineers, analysts, business teams, and the AI tools they use every day
Make data trustworthy at scale: build the systems that make data reliable, not just available. Implement data observability, quality frameworks, monitoring and alerting that give every data consumer confidence in what they work with, and give the team visibility to catch problems before they become incidents
Steward infrastructure, developer experience, and governance: take responsibility for how the platform is built and operated — infrastructure-as-code (Terraform/Terragrunt), CI/CD for data workflows, access management, security configurations, audit trails, and spend governance. Build the golden-path templates and patterns that make it fast and safe for engineers across the company to get value from data
Cloud data infrastructure experience: solid, hands-on experience building and operating cloud-based data infrastructure; comfortable with infrastructure-as-code (Terraform/Terragrunt), CI/CD for data workflows, and container technologies (Docker/Kubernetes)
Python proficiency: writes production-quality Python code. Python is our primary language on the Data Platform; familiarity with Go or Java is a plus but not required
What they're looking for
- Reliability and operational excellence: brings a reliability mindset to data: builds for observability, writes meaningful alerts, owns systems in production, and turns incidents into durable improvements
- A platform-product mindset: treats the engineers, analysts, and teams who build on the platform as primary users; thinks deeply about developer experience and reduces friction proactively
- Clear, cross-functional communication: communicates effectively across different audiences, actively seeks feedback from data consumers, and uses that input to improve the platform
- AI-native velocity: actively uses AI-powered tools to move faster and take on harder problems, freeing focus for the decisions that matter: architecture, system design, and the tradeoffs that determine whether a platform scales gracefully
