We power people’s progress. At Preply, we’re all about creating life-changing learning experiences. We help people discover the magic of the perfect tutor, craft a personalized learning journey, and stay motivated to keep growing. Our approach is human-led, tech-enabled — and it’s creating real impact. So far, 90,000 tutors have delivered over 20 million lessons to learners in more than 175 countries. Every Preply lesson sparks change, fuels ambition, and drives progress that matters.
Meet the team!
At Preply, the Data ingestion and enrichment team provides a single, trusted, and scalable data foundation. The team ensures that all analytics, machine learning, and product features are built on unified, governed, and production-grade data assets in Preply’s Lake House, including the extraction, normalization, and generation of structured data from Preply’s unstructured assets, forming a durable data moat for AI-driven products.
As a Senior Data Engineer in the Data Ingestion and Enrichment team, you will design and own the data layer that powers both Preply’s analytics, machine learning, and product. You will work closely with ML Platform, Applied/Data Scientists, Analytics Engineering, and Product squads to ensure that features, datasets, and pipelines are production-ready, observable, and reusable across the company. This role combines hands-on engineering with technical leadership.
What you’ll be doing:
Build trusted ingestion & enrichment foundations (Data Lake and Data as a Product):
Design, build, and own Preply’s data lake. Ensure every dataset has clear ownership, purpose, schemas, and quality expectations from first ingestion through downstream consumption by analytics, product, and ML teams. Treat trust, correctness, and predictability as first-class features of the platform.
Own end-to-end ingestion pipelines (batch & streaming):
Develop and operate scalable, reliable batch and streaming ingestion pipelines that support both real-time and analytical use cases. Design clear raw → standardized → consumption layers with explicit responsibilities, lineage, and retention strategies. Balance performance, cost, and reliability as the platform scales.
Data quality, contracts & early validation:
Define and implement data contracts between producers and consumers, covering schema, freshness, volume, and quality guarantees. Embed validation, anomaly detection, and quality checks early in the ingestion lifecycle to catch issues before they propagate. Standardize how quality metrics are measured, monitored, and surfaced across the platform.
Enrichment, modeling & lifecycle management:
