Staff Analytics Engineer
Role details
Job location
Tech stack
Job description
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.
Requirements
Do you have experience in Taxonomy?, * 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.