Data Platform Engineer

Role overview This senior, hands-on engineering role treats data infrastructure as a product, defining how billions of records are structured, indexed, and exposed across an analytics platform serving global brands and agencies. The position sits on a Core team that owns data foundations, backend systems, and shared infrastructure powering products, analytics, and AI capabilities.

Responsibilities - Own and optimize MongoDB clusters and OpenSearch indexes that house billions of documents, including sharding strategies and indexing patterns for search performance - Design and implement a multi-tier storage strategy, deciding which data stays "hot" in production systems and which moves to cold or analytical storage for AI and R&D use - Build and maintain internal APIs that abstract database complexity for product, analytics, and AI teams - Lead schema evolution and design no-downtime migration paths across production data stores at scale - Collaborate with Product, Backend, AI/R&D, and Analytics teams to turn research initiatives into production-ready systems

Requirements - Strong backend engineering skills combined with deep expertise in data platforms, data lakes, and large-scale data systems - Hands-on experience operating MongoDB and OpenSearch (or similar) in high-volume production environments - Proven ability to design migrations and storage strategies across billions of records without downtime - Experience building internal data access layers or APIs for analytics and ML consumers - Comfort working cross-functionally with research, product, and analytics stakeholders

Benefits and work setup - Fully remote role with flexible core hours within European time (9am-6pm, 4 required hours per day) - Comprehensive medical insurance for all team members - Periodic in-person gatherings through short-term remote offices in European locations