Lead Data Analytics Engineer

Overview About Landbot Landbot operates in more than 80 countries and offers a platform that helps companies create chatbot conversations across Web, WhatsApp, and Messenger. We’re building a high-performance team that blends engineering excellence, product mindset, and customer obsession. We are looking for a Lead Data Analytics Engineer to help scale our platform and deliver real impact. About the team Human-first: Empathy, respect, and diversity as drivers of innovation. Customer-centric: Value first; progress measured by real impact, not vanity metrics. Collaboration: One team, shared decisions, and early draft culture. Unity: Trust, psychological safety, and continuous improvement as teams of teams. Trust & commitment: Open debate, then full support once decisions are made. High performance: Accountability, resilience, and daily improvement. Adaptability: Curiosity, constant learning, and just enough process to stay agile. Challenging mindset: Question assumptions and the status quo to keep evolving. About the Position At Landbot, data is business and one of our most strategic assets. We’re building a single source of truth that powers decision-making, product innovation, and customer value across the company. As a Data Analytics Engineer, you will sit at the intersection of engineering, product, and business, transforming raw data into certified, business-ready models and enabling teams to create impact through insights. This role is part of our central Data Analytics team, working closely with Data Engineers (Platform) and stakeholders across Product, Sales, Customer Success, and Marketing. Your mission: make data reliable, accessible, and actionable, fueling both day-to-day operations and our long-term AI-driven vision. Responsibilities Transform raw data into business-ready models using dbt, powering Landbot’s single source of truth. Define, standardize, and certify key business metrics, ensuring consistency across teams. Enable 70% self-service analytics by building certified datasets, governed layers, and training business teams. Partner with Business, GTM, CS, and Marketing to validate hypotheses, run experiments (A/B tests), and translate insights into decisions. Deliver high-leverage insights with productivity in mind, focusing on what drives action rather than producing endless reports. Turn data into stories: craft clear, compelling visualizations and narratives that influence product strategy and business growth. Support advanced analytics use cases such as segmentation, retention analysis, and predictive modeling to prepare for AI-powered features. Collaborate with Data Engineers and Platform to ensure reliable, scalable, and observable pipelines. Promote data literacy: coach and empower non-technical teams to ask better questions and use data in daily decisions. Demonstrate proficiency with analytics engineering tools (dbt, SQL, Airflow) and modern data warehouses (BigQuery, Snowflake, Redshift). Strong Python skills for building transformations, automation, and data quality validations. Translate business metrics into decisions that impact ARR, retention, and growth. Provide storytelling-driven insights and visualizations to influence product strategy. Encourage governance and trust in data, ensuring consistency and quality in all builds. Collaborate with engineers, PMs, and business teams in a cross-functional environment. Foster a foundation for AI-ready data products. Proficient with Git / GitLab, applying best practices in version control and GitOps workflows for reproducible and collaborative development. Qualifications Experience with analytics engineering tools (dbt, SQL, Airflow) and modern data warehouses (BigQuery, Snowflake, Redshift). Strong Python skills for data transformations and validations. Fluency in business metrics and ability to translate data into decisions impacting ARR, retention, and growth. Storytelling with data: ability to turn complex data into clear, persuasive insights and visualizations. Productivity mindset: focus on the 20% of insights that deliver 80% of value. Experience with experiments (A/B tests, hypothesis validation) and bridging product discovery with data. Ability to work in a cross-functional environment with engineers, PMs, and business teams. Commitment to data governance and trust, ensuring data quality and consistency. Curiosity, growth orientation, and interest in building AI-ready data foundations. Proficiency with English and Spanish communication. Qualifications (continued) We require eligibility to work in Spain and are open to collaboration agreements. Benefits Hybrid work model: flexibility to work remotely, from our Barcelona office, or a combination of both. Collaborative work environment. Flexible working hours. Paid time off and flexible holidays: 26 paid days per year (23 regular days + December 24th & 31st), plus one additional day off on your birthday. Annual budget for training and professional development. Transportation ticket. English and Spanish lessons. Flexible compensation plan through Cobee. Team-building activities. Referral program with bonuses for bringing in talented professionals. How to Apply Please submit your résumé along with a cover letter explaining why you are the ideal candidate for Landbot and this role. We look forward to hearing from you #J-18808-Ljbffr