preply - Staff Analytics Engineer
Requirements
• 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). • 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. • Care to change the world - We are passionate about our work and care deeply about its impact to be life changing. • We do it for learners - For both Preply and tutors, learners are why we do what we do. Every day we focus on empowering tutors to deliver an exceptional learning experience. • Keep perfecting - To create an outstanding customer experience, we focus on simplicity, smoothness, and enjoyment, continually perfecting it as every detail matters. • Now is the time - In a fast-paced world, it matters how quickly we act. Now is the time to make great things happen. • Disciplined execution - What makes us disciplined is the excellence in our execution. We set clear goals, focus on what matters, and utilize our resources efficiently. • Dive deep - We leverage business acumen and curiosity to investigate disparities between numbers and stories, unlocking meaningful insights to guide our decisions. • Growth mindset - We proactively seek growth opportunities and believe today's best performance becomes tomorrow's starting point. We humbly embrace feedback and learn from setbacks. • Raise the bar - We raise our performance standards continuously, alongside each new hire and promotion. We build diverse and high-performing teams that can make a real difference. • Challenge, disagree and commit - We value open and candid communication, even when we don’t fully agree. We speak our minds, challenge when necessary, and fully commit to decisions once made. • One Preply - We prioritize collaboration, inclusion, and the success of our team over personal ambitions. Together, we support and celebrate each other's progress. • Diversity, Equity, and Inclusion
Responsibilities
• 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.
Benefits
• Leadership in the design and evolution of core analytical data models across key business domains for clarity, scalability, and long-term sustainability. • Defining and championing company-wide analytics engineering standards including modeling patterns, naming conventions, testing strategies, documentation to ensure consistency and best practices are followed throughout the organization. • Building and optimizing robust ETL/ELT pipelines capable of handling multi-terabyte data volumes with high reliability and performance for efficient data processing needs. • Ownership and evolution of BI and semantic layer (Looker / LookML) to enable intuitive, performant, self-service analytics across the company ensuring ease of accessibility in decision making processes. • Partnering closely with Data Scientists, Product Managers, and Engineers for streamlining workflows which reduces duplicated logic supporting a single source of truth (SSOT). • Driving initiatives focused on data quality, reliability, and governance to ensure that the decision-critical datasets are trustworthy and well documented.
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