• Data Products: Design and implement scalable data models (Silver/Gold layers), treating datasets as internal products with a strong focus on usability • Engineering: Develop robust pipelines using software engineering best practices (versioning, modularization, and CI/CD). • Governance: Establish and monitor quality standards, lineage, and data cataloging, acting as the guardian of the "single source of truth". • Democratization: Advance the Self-Service Analytics ecosystem by creating semantic layers that reduce business teams' technical dependencies. • Discovery: Operate at the intersection of Data Engineering and Business, translating complex questions into solid, viable data architectures. • Evangelization: Promote a data-driven culture and data literacy, empowering colleagues to use information efficiently and apply good analytical practices.

Role Description We are looking for an Analytics Engineer to help scale the "engine" behind our business intelligence — at a moment when that engine is expanding its mandate. - Partner with data and engineering teams, as well as backend engineers, Sales/RevOps, Marketing, and Product and AI Engineering stakeholders. - Build reliable, high-performance pipelines while contributing to ingestion, orchestration, and LLM-powered classification workflows. - Opportunity to shape and own the role as the BI team evolves into an Analytics & Data Intelligence function. Qualifications - Significant experience building and managing complex dbt projects. - Deep experience with Google BigQuery (or similar cloud warehouses). - Proficient in SQL and Python, with the ability (or strong interest) to work with Go. - Disciplined about documentation, data contracts, and establishing clear lineage. - Experience with Airflow and containerization (Docker) is a significant plus. - Excited about using modern tools (like BQML or Vertex AI) for automation. - Strong communicator who enjoys partnering with cross-functional stakeholders. Requirements - Build modular, well-tested SQL models using dbt. - Maintain and optimize custom ingestion workflows and orchestration. - Implement internal automation workflows that leverage LLMs for data classification. - Own the "Reverse ETL" flows that push critical business signals back into GTM tools. - Contribute to engineering culture through code reviews and documentation. Benefits - Private life and health insurance plan. - Fully remote work if preferred, apart from team meetings a few times per year. - Your personal annual training budget. - An annual home office allowance to set up your personal space. - Company laptop. - 23 days of paid time off. - 3 early summer Fridays in July and August. - Access to AI tools at work. - A free LearnWorlds School to build and sell your own courses. - Work in one of the globally top 5 e-learning courses platforms. - An opportunity to grow alongside us and shape the look and feel of tomorrow's e-learning. - An entrepreneurial, international, and highly motivated team with a flat hierarchy. - Annual company retreats.

• Liaise with different teams to understand business needs and propose optimal data modeling solutions; • Translate business concepts into quantitative metrics (KPIs, OKRs, etc.); • Model and architect data for analytical purposes following best practices, including data quality and documentation; • Orchestrate workflows related to data pipelines.

• Lead analytics engineering strategy and execution, owning the design and evolution of core data models using modern practices — Data Vault, dimensional modeling, dbt on Snowflake — with a clear roadmap toward a Databricks lakehouse architecture. • Own the company-wide semantic layer and metrics store, ensuring Bookings, ARR, NDRR, and other critical business metrics have a single, version-controlled, trusted definition consumable by every downstream tool and AI agent. • Drive Finance Analytics, including ownership of Bookings, ARR, NDRR, segment and territory reporting, and month-end close pipelines, partnering closely with Finance and Revenue Operations. • Set the standard for data governance and data quality, including discoverability, lineage, access controls, and data contracts between upstream producers and downstream consumers — leveraging Atlan, Unity Catalog, and Monte Carlo. • Own data egress and reverse-ETL strategy, governing pipelines from the data warehouse to downstream platforms including Salesforce, Marketo, Gainsight, Outreach, Thoughtspot, Tableau, and Amplitude. • Shape our AI data strategy, ensuring data assets are structured, documented, and governed to serve as reliable foundations for AI agents, LLM-based analytics, and intelligent product features — while driving data-as-a-product principles and platform cost discipline. • Lead cross-functional strategic programs including Quote-to-Cash modernization, unified customer data modeling, and the Snowflake-to-Databricks migration, acting as a key decision-maker across multi-quarter initiatives. • Develop cross-team relationships across functional leadership and BI partners to ensure we are meeting existing analytics needs and are well-positioned to meet the needs of the future. • Build, develop, and lead a high-performing team, managing vendor and partner relationships, and evolving team capabilities to meet the demands of a rapidly maturing data platform.