preply - Data Engineer

• Contribute to trusted ingestion & enrichment foundations (Data Lake and Data as a Product): • Build and maintain components of 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. • Develop end-to-end ingestion pipelines (batch & streaming): • Data quality, contracts & early validation: • 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. Apply standardized quality metrics. • Enrichment, modeling & lifecycle management: • Build enrichment logic that joins, standardizes, and contextualizes data across domains using shared definitions and reusable patterns. Support historical tracking, point-in-time correctness, and dataset versioning so downstream users can confidently analyze changes and impacts over time. • Observability, reliability & operational excellence: • Instrument ingestion pipelines with strong observability: freshness, latency, data quality, and cost metrics. Contribute to SLOs, alerting, and incident response playbooks so data failures are visible, diagnosable, and recoverable. Help move the platform from reactive firefighting to proactive reliability management. • Governance & compliance by design: • Enable self-service & standardization: • Contribute to standardized ingestion templates, shared libraries, and platform tooling that enable teams to onboard new data sources independently. Improve discoverability, documentation, and metadata so datasets you own are easy to find and trust without relying on tribal knowledge. • Cross-team collaboration & ownership: • Work closely with Product, Backend, Analytics, and ML partners to align on ingestion requirements and trade-offs. Build strong working relationships across teams. Mentor junior team members and actively contribute to a culture of shared data quality standards and data contracts.