Staff Analytics Engineer

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.

At Preply, data is foundational to how we build, experiment, and scale our product. We run hundreds of A/B tests at any given time, power a complex two-sided marketplace, and increasingly rely on data to enable AI-driven personalization and decision-making.

Our analytics platform supports teams across Product, Growth, Finance, and Engineering/AI, and its quality directly impacts the speed and confidence of company-wide decisions.

As a Staff Analytics Engineer, you will play a critical role in shaping the analytical foundations of Preply. You’ll operate at a company-wide level, setting standards, designing scalable data models, and influencing how analytics engineering is practiced across teams. This is a role for someone who wants to build systems that others build on.

Our Data Team is dedicated to empowering top-quality decision-making. Do you want to know how? Visit our Tech Radar to learn about the technologies we use at Preply!

What you’ll be doing

• Lead the design and evolution of core analytical data models across key business domains, ensuring clarity, scalability, and long-term sustainability.
• Define and champion analytics engineering standards (modeling patterns, naming conventions, testing strategies, documentation) used across the organization.
• Build and optimize robust ETL/ELT pipelines that handle multi-terabyte data volumes with high reliability and performance.
• Own and evolve our BI and semantic layer (Looker / LookML), enabling intuitive, performant, and truly self-service analytics.
• Partner closely with Data Scientists, Product Managers, and Engineers to streamline analytical workflows and reduce duplicated logic (SSOT).
• Drive initiatives focused on data quality, reliability, and governance, ensuring decision-critical datasets are trustworthy and well-documented.
• Influence company-wide data strategy to support rapid product experimentation, marketplace growth, and large-scale personalization.
• Work closely with the engineering team to provide valuable data products for Experimentation, Engineering and Applied AI teams.
• Act as a technical leader and mentor within the Analytics Engineering discipline, raising the bar through example, reviews, and architectural guidance.

What you need to succeed

• Extensive experience in analytics engineering, data engineering, or related roles, operating on complex, high-impact data systems.
• Expert-level proficiency in SQL, with experience using Python in data workflows.
• Proven track record of designing scalable analytical data models that support experimentation, reporting, and strategic decision-making.
• Advanced hands-on experience with dbt, Looker, Airflow, or similar tools.
• Deep understanding of data modeling best practices, analytics architecture, and self-service BI platforms.
• Strong business acumen, with the ability to translate ambiguous problems into clear, data-backed solutions.
• Exceptional communication skills, with the ability to influence and align stakeholders across technical and non-technical teams.
• A proactive, strategic mindset, you look beyond immediate tasks to improve systems, standards, and long-term outcomes.
• Fluency in English (C1 level or above).

Nice to have

• Experience scaling data platforms in high-growth or post-Series C startups.
• Proven experience defining and standardizing event taxonomies, KPIs, and canonical metrics.
• Strong experience working with AWS or Google Cloud data ecosystems.
• Previous experience mentoring or coaching other data professionals.

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