Staff Analytics Engineer

• 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. #LI-VL1

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