Описание

Preply creates personalized language-learning experiences by connecting learners with tutors. Its human-led, technology-enabled platform serves learners in 180 countries, with over 100,000 tutors teaching more than 90 languages. The Data Ingestion and Enrichment team provides a trusted, scalable data foundation for analytics, machine learning, and product features through governed, production-grade data assets.
Задачи

Build and own Preply’s data lake and trusted ingestion and enrichment foundations
Develop and operate scalable batch and streaming ingestion pipelines for real-time and analytical use cases
Design raw, standardized, and consumption data layers with clear responsibilities, lineage, and retention strategies
Define and implement data contracts covering schemas, freshness, volume, and quality guarantees
Embed validation, anomaly detection, and quality checks early in the ingestion lifecycle
Standardize how quality metrics are measured, monitored, and surfaced
Build enrichment logic that joins, standardizes, and contextualizes data across domains




Support historical tracking, point-in-time correctness, and dataset versioning
Instrument ingestion pipelines with freshness, latency, data quality, and cost metrics
Contribute to SLOs, alerting, and incident response playbooks
Apply access control, classification, privacy protections, masking, minimization, and anonymization at ingestion time
Contribute to standardized ingestion templates, shared libraries, and platform tooling
Improve dataset discoverability, documentation, and metadata
Collaborate with Product, Backend, Analytics, and ML partners on ingestion requirements, trade-offs, and priorities
Promote shared ownership of data quality and platform standards
Требования

Experience building architectural patterns for large, high-scale applications, including well-designed APIs, high-volume data pipelines, or efficient algorithms
Solid experience in platform or data engineering teams, or equivalent

📌 data engineer for an educational project (Madrid)
🏢 Enfint
📍 Madrid