Senior II - Data Ingestion and Enrichment team Location: London

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 personalised learning journey, and stay motivated to keep growing. Our approach is human‑led, tech‑enabled – and it’s creating real impact. We’ve just reached unicorn status with a $150M Series D, accelerating our vision to transform education through human‑led, AI‑enhanced learning. Today, 100,000+ tutors teach 90+ languages to learners in 180 countries – and we’re only getting started. As a category‑defining company, we’re shaping what the future of learning looks like at global scale. 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.

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. Standardise how quality metrics are measured, monitored, and surfaced across the platform.
• Enrichment, modelling & lifecycle management: Build enrichment logic that joins, standardises, and contextualises data across domains using shared definitions and reusable patterns. Support historical tracking, point‑in‑time correctness, and dataset versioning so downstream users can confidently analyse 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, minimised, or anonymised by default, and that all data flows are auditable and traceable. Make governance invisible to users but deeply embedded in platform workflows.
• Enable self‑service & standardisation: Contribute to standardised ingestion templates, shared libraries, and platform tooling that enable teams to onboard new data sources independently within clear guardrails. Improve discoverability, documentation, and metadata so datasets are easy to find, understand, 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, trade‑offs, and priorities. Promote shared ownership of data quality and platform standards, and help foster a culture where teams move fast together under common data contracts and principles.

What you need to succeed:

• Driving architectural patterns of a large, high‑scale application (e.g., well‑designed APIs, high‑volume data pipelines, efficient algorithms).
• Solid experience working in platform or data engineering teams (or equivalent impact) with evidence of leading multi‑stakeholder deliveries.
• Familiarity with cloud platforms (AWS/GCP or equivalent) and modern DevOps practices.
• Hands‑on experience designing and implementing real‑time and batch data processing infrastructures using modern frameworks like Spark, Flink, Spark streaming, Kafka, Debezium, etc.
• Expertise 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+).

Nice to have:

• Proven track record in scaling data infrastructures within fast‑growing startups.
• Terraform/Kubernetes for data tooling.
• SQL proficiency.

Why you’ll love it at Preply:

• An open, collaborative, dynamic, and diverse culture.
• A generous monthly allowance for lessons on Preply.com, a Learning & Development budget, and time off for your self‑development.
• A competitive financial package with equity, leave allowance, and health insurance.
• Access to free mental health support platforms.
• The opportunity to unlock the potential of learners and tutors through language learning and teaching in 175 countries (and counting!).

Diversity, Equity, and Inclusion

Preply.com is committed to creating an inclusive environment where people of diverse backgrounds can thrive. We believe that the presence of different opinions and viewpoints is a key ingredient for our success as a multicultural Ed‑Tech company. That means that Preply will consider all applications for employment without regard to race, color, religion, gender identity or expression, sexual orientation, national origin, disability, age or veteran status.