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Preply is a unicorn edtech company (Series D, $150M) that connects 100,000+ tutors teaching 90+ languages to learners in 180 countries. The company is building human-led, AI-enhanced learning experiences at global scale.
The Data Platform team provides the foundational data infrastructure for all analytics, machine learning, and product features across Preply. The team owns end-to-end batch and streaming ingestion pipelines, data contracts, quality checks, data modeling, governance, and self-service tooling that enables other teams to onboard new data sources safely. The platform serves as a critical dependency for ML, Analytics Engineering, Data Science, Backend, and Product teams.
As Senior Engineering Manager for Data Platform, you will lead and grow a high-performing data engineering team while owning the engineering strategy, execution, and organizational health of this critical platform. This is a hands-on leadership role requiring strong technical judgment combined with people management excellence.
Key responsibilities include:
- Lead and develop a data engineering team, fostering ownership, continuous improvement, and technical excellence through coaching, mentorship, and regular feedback
- Define and drive the long-term vision for Preply's data foundation (Lakehouse, ingestion layers, data contracts, enrichment, governance) aligned with company objectives
- Identify and prioritize high-impact opportunities in partnership with Analytics, ML Platform, Data Science, and Product teams
- Own execution and delivery: translate strategy into realistic plans, balance foundational investment with near-term needs, surface trade-offs, and maintain speed, quality, cost efficiency, and predictability
- Provide technical leadership on architecture across batch and streaming ingestion, data modeling, quality, and governance
- Establish SLOs, observability, and incident response for data pipelines to move from reactive firefighting to proactive reliability management
- Champion engineering best practices and drive continuous improvements in code quality, operational excellence, and development processes
- Collaborate cross-functionally with stakeholders across the business to maximize impact and align on priorities, data ownership, and standards
- Own hiring quality, team composition, and operating model as the platform and company needs evolve
Preply engineers have direct access to AI coding tools (90%+ adoption) and the freedom to experiment. The company shares technical insights on its Engineering Blog and maintains a public Tech Radar.
Requirements:
- 8+ years of software or data engineering experience, including 5+ years in engineering management
- Demonstrated experience leading data or platform engineering teams
- People-centric approach and proven team-building skills; passion for building high-performing teams and helping people grow
- Strong technical judgment and systems thinking; comfort driving architectural discussions and evaluating technical trade-offs
- Track record of hiring, coaching, and retaining high-performing teams, including senior engineers
- Ability to work closely with Analytics, Data Science, ML, and Product partners to identify opportunities and drive measurable outcomes
- Clear communication and stakeholder leadership; ability to align teams with different incentives and make sound decisions with incomplete information
- Results-oriented with a track record of driving performance and motivating teams to achieve ambitious, business-focused goals
- Ethical leadership: lead by example with empathy, transparency, accountability, and strong ownership; create psychological safety for teams to challenge ideas and grow
Nice to have:
- Hands-on familiarity with modern data stack (Spark, Flink, Kafka, Debezium, Airflow, dbt) and cloud platforms (AWS/GCP)
- Experience leading teams that built data platforms used by multiple teams in fast-growing companies
- Experience running a data platform as a product (data contracts, self-service onboarding, discoverability, adoption metrics)
- Experience with data governance, privacy, and compliance (classification, access control, masking, auditability)
- Familiarity with feature stores, ML data pipelines, or preparing data for GenAI/RAG use cases
- AI expertise: use of Agentic AI beyond code generation for architecture, planning, and decision-making while retaining human judgment for critical decisions
