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Preply is a unicorn edtech platform (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, ML, and product features across Preply. The team owns end-to-end batch and streaming ingestion pipelines, data contracts, quality checks, data modeling, and self-service tooling that enables other teams to onboard new data sources safely. The platform serves as the backbone for AI-driven products and features a Lakehouse architecture with unified, governed, production-grade data assets.
As Senior Engineering Manager for Data Platform, you will lead and grow a high-performing data engineering team while owning the long-term technical vision for Preply's data foundation. You'll define strategy for the Lakehouse, ingestion layers, data contracts, enrichment, and governance—ensuring alignment with company objectives and the needs of analytics, ML, and product teams. Key responsibilities include:
- Leading team growth, fostering ownership, continuous improvement, and technical excellence; coaching and developing engineers through mentorship and regular feedback
- Defining and driving the long-term vision for Preply's data platform, ensuring it meets the needs of consuming teams
- Identifying and prioritizing high-impact opportunities in partnership with Analytics, ML Platform, Data Science, and Product teams
- Owning execution and delivery: translating strategy into realistic plans, balancing foundational investment with near-term needs while maintaining speed, quality, cost efficiency, and predictability
- Providing technical leadership on batch and streaming ingestion, data modeling, quality, and governance; helping the team navigate trade-offs and remove obstacles
- Establishing SLOs, observability, and incident response for data pipelines to move from reactive firefighting to proactive reliability
- Championing engineering best practices and driving continuous improvements in code quality, operational excellence, and development processes
- Collaborating cross-functionally to maximize impact and align on priorities, data ownership, standards, and outcomes
- Owning hiring quality, team composition, and operating model as the platform and company evolve
Preply has achieved 90%+ adoption of AI coding tools across engineering and is moving toward more autonomous, AI-augmented development at scale. You'll help your team adopt these ways of working while setting appropriate standards and guardrails.
REQUIREMENTS:
- 8+ years of software or data engineering experience, including 5+ years in engineering management, with experience leading data or platform engineering teams
- People-centric approach and team-building skills: passion for building high-performing teams and helping people grow; ability to create alignment, foster psychological safety, provide actionable feedback, and develop engineers through coaching and mentorship
- Strong technical judgment and systems thinking; comfort driving architectural discussions, evaluating technical trade-offs, championing engineering best practices, and partnering with senior engineers on technical strategy
- Track record of hiring, coaching, and retaining high-performing teams, including senior engineers
- Product & Business Partnership: comfort working closely with Analytics, Data Science, ML, and Product partners to identify opportunities, prioritize investments, and drive measurable outcomes; ability to connect data platform decisions to downstream user experience, business metrics, and company strategy
- Clear communication and stakeholder leadership; ability to align teams with different incentives, make sound decisions with incomplete information, and revisit assumptions as the data ecosystem changes
- Results-oriented with a track record of driving performance; ability to motivate teams to set and achieve ambitious, business-focused, measurable goals
- Ethical leadership: lead by example as the team's moral compass; demonstrate empathy, transparency, accountability, and strong ownership; value honesty and fairness; create a culture where people feel safe to challenge ideas, learn, and grow
NICE TO HAVE:
- Hands-on familiarity with the modern data stack (Spark, Flink, Kafka, Debezium, Airflow, dbt, or similar) and cloud platforms (AWS/GCP or equivalent) and modern DevOps practices
- 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, and measuring success through adoption and consumer outcomes
- Experience with data governance, privacy, and compliance at ingestion time (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 to support architecture, planning, and decision-making, while retaining human judgment for architecture, people decisions, business logic, and security; ability to establish quality gates and safety nets for team workflows
