Meet DeepL
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 translation, improved writing and real-time voice translation.
What sets us apart
What sets us apart is our blend of cutting-edge AI technology, meaningful work, and a culture where people truly thrive. We’re a team of innovators, researchers, and creators driven by a shared purpose to unlock human potential by making work simpler, smarter, and more connected.
Meet the team behind this journey
DeepL is not just a translation tool; we are building the Operating System for Global Communication. We are the rare AI company that builds the entire stack in-house: from training next-gen LLMs on our own NVIDIA DGX SuperPODs to delivering real-time Language AI to over 300 million users and 200,000+ businesses globally.
The Data Platform team is the engineering foundation that makes that possible. We build and operate the infrastructure that the entire company relies on to work with data effectively — ingestion infrastructure, a reliable lakehouse, the tooling that data engineers build on top of, and increasingly, AI-powered interfaces (MCP connectors, workflow skills, and integrations) that bring data directly into how people and AI agents get work done across DeepL.
Your responsibilities
- 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
What we offer
- Diverse and internationally distributed team: joining our team means becoming part of a large, global community with people of more than 90 nationalities.
- Open communication, regular feedback: we value smooth collaboration, direct and actionable feedback, and believe that leading with empathy and growth mindset makes us better together.
- Hybrid work, flexible hours: we offer a hybrid work schedule, with team members coming into the office twice a week. With flexible working hours and trust in your productivity, we are in sync with your team’s general locations and time zones to foster effective and seamless collaboration.
- Virtual Shares: An ownership mindset in every role. We believe everyone should share in our success, and that’s why every employee receives Virtual Shares, linking your contribution directly to DeepL’s growth and rewarding you with a stake in our future.
- Regular in-person team events: we bond over vibrant events that are as unique as our team, from local team and business unit gatherings, to new-joiner onboardings, to company-wide events that bring us all together.
- Monthly full-day hacking sessions: every month, we have Hack Fridays, where you can spend your time diving into a project you're passionate about and get the opportunity to work with other teams.
- 30 days of annual leave: we value your peace of mind. With 30 days off (excluding public holidays) and access to mental health resources, we make sure you're as strong mentally as you are professionally.
- Competitive benefits: just as our team spans the globe, so does our benefits package. We've crafted it to reflect the diversity of our team and tailored it to align with your unique location.
Must-have
- 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
- 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
Expected
- 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
Nice-to-have
- Lakehouse and streaming technologies — experience with Databricks, Apache Iceberg, Kafka, or similar is a strong advantage
