Senior Data Platform Engineer

Join Proton and build a better internet where privacy is the default

Proton was founded in 2014 by scientists from CERN on a simple truth:
privacy is a fundamental human right
. Since then, we’ve built the world’s largest encrypted email service (Proton Mail) and expanded into Proton VPN, Proton Drive, Proton Pass, and Proton Calendar—tools used by millions globally to protect their freedom, fight censorship, and keep their data safe. In some situations, Proton has literally helped save lives!

We are profitable, independent (no VC control), and selectively hire from the top ~1% of applicants. Our 700+ team members across 50+ countries come from leading organizations and elite academic backgrounds. We move fast, keep hierarchy light, and prioritize impact over optics. If you want to do meaningful work with exceptionally high-caliber people, this is it. Join us and do work you can truly be proud of.

Check our open-source projects here!

The Proton Data team is responsible for everything that enables the company to make data-driven decisions, with our on-premise, custom data platform at the center. We are looking for a Senior Data Platform Engineer
, to help maintain, improve and consolidate the data pipelines in our data platform, and help it take our data platform to the next level.

• Drive end-to-end data engineering and data platform initiatives, from problem discovery and technical design to implementation, rollout, documentation, and long-term ownership.
• Design, build, and operate reliable, scalable, and maintainable data pipelines that support analytics, product insights, experimentation, and business‑critical reporting.
• Work with large-scale batch and streaming data processing systems, ensuring data is accurate, timely, observable, and easy to consume.
• Collaborate closely with engineering, product, analytics, data science, and business stakeholders to understand needs, clarify requirements, and translate them into pragmatic technical solutions.
• Improve the reliability, performance, and operational maturity of the data platform, including monitoring, alerting, data quality checks, failure handling, and incident response.
• Contribute to the evolution of our data architecture, including data modeling, warehouse/lakehouse practices, schema design, data contracts, ingestion patterns, and platform standards.
• Help identify gaps in our current data ecosystem and propose scalable solutions that reduce complexity, improve developer experience, and enable teams to move faster.
• Lead technical discussions and contribute to design reviews, architecture decisions, and roadmap planning for data platform initiatives.
• Mentor and support other engineers through code reviews, pairing, technical guidance, and knowledge sharing.
• Promote strong engineering practices across the team, including testing, documentation, observability, maintainability, and thoughtful trade‑off analysis.
• Take ownership of ambiguous or cross‑functional problems and help turn them into clear, actionable initiatives with measurable impact.

• Master’s degree in Computer Science, Engineering, Data Engineering, or a related technical discipline, or equivalent practical experience.
• 5+ years of relevant professional experience in data engineering, data platform engineering, backend engineering, distributed systems, or a closely related field.
• Strong software engineering skills, with excellent knowledge of Scala and/or Java, as well as solid Python experience. Experience writing production‑grade, maintainable, and well‑tested code is expected.
• Strong understanding of distributed systems, including scalability, fault tolerance, consistency trade‑offs, resource management, and operational failure modes.
• Solid experience designing, building, and operating data pipelines in production environments, including batch and/or streaming workloads.
• Experience with Apache Spark is highly recommended, especially in large‑scale data processing environments.
• Strong knowledge of databases, SQL, query optimization, data modeling, and data access patterns.
• Good understanding of data warehouse and data lakehouse best practices in Big Data environments,…