Senior Data Platform Engineer

About the role

DeepL is seeking a Senior Data Platform Engineer to take ownership of the critical data infrastructure that powers language AI for millions of users worldwide. In this role, you will design and maintain the pipelines and platforms that ensure data reliability, security, and scalability across the organization. You will be responsible for building robust solutions that empower internal teams to derive insights and drive innovation through data. This position requires a proactive approach to solving complex engineering challenges while collaborating closely with data scientists, analysts, and other engineers. You will work on systems that handle large-scale data processing with a focus on performance and efficiency. Your work will directly support DeepL's mission by enabling the AI products that over 100 million users rely on every day. The successful candidate will have the opportunity to shape the future of the data platform and contribute to cutting-edge AI workflows. This is a hands-on role where your expertise will help maintain the backbone of DeepL's data-driven decision-making.

Key facts

• DeepL provides 30 days of paid annual leave, in addition to public holidays.
• Employees are granted Virtual Shares, aligning their contributions with the company's growth trajectory.
• The organization promotes a hybrid working model, requiring two days in the office each week, along with flexible working hours.
• Team members can participate in regular in-person events and monthly full-day hackathons to foster collaboration and innovation.
• Benefits packages are customized to suit individual locations, ensuring relevance and support for all employees.
• DeepL boasts a diverse and international workforce, with team members located in the UK, Germany, Netherlands, Poland, the US, and Japan.

What you'll do

Architect and implement scalable data platform components that meet the evolving needs of DeepL's language AI products and internal stakeholders.

Lead the development and maintenance of data pipelines, ensuring high availability, fault tolerance, and optimal performance across distributed systems.

Collaborate with cross-functional teams to translate business requirements into robust data solutions, enabling analytics and AI initiatives.

Monitor and optimize the performance of existing data infrastructure, identifying bottlenecks and implementing improvements proactively.

Ensure data security, governance, and compliance by implementing best practices and adhering to organizational standards and regulations.

Work closely with data scientists and analysts to provide reliable datasets and tools that accelerate insight generation and decision-making.

Design and maintain data models, schemas, and storage solutions that support efficient querying and data accessibility.

Troubleshoot complex issues in the data platform, coordinating with other engineers and stakeholders to resolve problems swiftly.

Contribute to the continuous integration and continuous deployment (CI/CD) processes for data-related projects, ensuring smooth and reliable updates.

Mentor junior engineers and share knowledge through code reviews, documentation, and technical discussions to strengthen the team.

Explore and evaluate new data technologies and tools, proposing innovations that can enhance the platform's capabilities and scalability.

Participate in on-call rotations to provide timely support and maintain the integrity of the data platform during critical incidents.

Engage in code reviews and pair programming sessions to uphold code quality and foster a culture of collective ownership.

Contribute to the broader data community within DeepL by sharing insights, best practices, and lessons learned from hands-on experience.

Requirements

Demonstrate a strong background in data engineering, with hands-on experience in building and maintaining data platforms and pipelines.

Show proficiency in programming languages commonly used for data engineering, such as Python or Scala, and a deep understanding of data structures and algorithms.

Bring experience with distributed systems and big data technologies, including but not limited to Spark, Kafka, or similar frameworks.

Exhibit solid knowledge of database systems, including relational databases, NoSQL databases, and data warehousing concepts.

Possess a thorough understanding of cloud platforms and their services, with experience in deploying and managing scalable infrastructure.

Have a proven track record of writing clean, maintainable, and efficient code, with a focus on reliability and performance.

Display strong problem-solving skills and the ability to debug complex issues in a distributed environment.

Communicate effectively with both technical and non-technical stakeholders, translating technical details into actionable insights.

Nice to have

Experience with containerization and orchestration tools such as Docker and Kubernetes.

Familiarity with data visualization tools and practices to support stakeholder engagement.

Knowledge of machine learning operations and the ability to work alongside data science teams.

Practical notes

This is a hybrid role requiring 2 days of in-office work per week.

The position is based in London and is open to full-time professional candidates.

Candidates must be eligible to work in the UK without sponsorship.