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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Preply Inc. - **Location:** London, UK - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Cloud Computing, Information Engineering, Data Sharing, DevOps, Machine Learning, Metadata, Azure Machine Learning, Data Streaming, Management of Software Versions, Web Application Frameworks, Workflow Management Systems, Data Ingestion, Apache Spark, Backend, Data Layers, Data Lakes, Debezium, Low Latency, Apache Flink, Apache Kafka, Spark Streaming, Data Pipelines - **Published:** July 24, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=94776515d5b71a35 ## About the Role * Hands-on experience building components of large, high-scale applications (e.g., data pipelines, well-structured APIs, efficient algorithms). * Solid experience working in platform or data engineering teams (or equivalent) with the ability to deliver within a multi-stakeholder environment. * Familiarity with cloud platforms (AWS/GCP or equivalent) and modern DevOps practices. * Hands-on experience designing and implementing real-time and batch data processing pipelines using modern frameworks like Spark, Flink, Spark Streaming, Kafka, Debezium, etc. * Experience with orchestration tools such as Airflow, dbt, or similar. * Exceptional problem-solving skills paired with a proactive, innovative mindset focused on continuous improvement. * Strong communication and cross-functional collaboration skills (English level B2+) ## Description 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 Data Engineer in the Data Ingestion and Enrichment team, you will build and contribute to 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 within the team. What you'll be doing: 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): Build and operate reliable batch and streaming ingestion pipelines that support both real-time and analytical use cases. Contribute to defining 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: 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: Apply consistent access control, classification, and privacy protections at ingestion time. Ensure sensitive data is properly masked, minimized, or anonymized by default, and that all data flows you own are auditable and traceable. 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [Top-Paying Tech Jobs (with Salaries)](https://www.wearedevelopers.com/magazine/372-top-paying-tech-jobs-with-salaries)