Lead Data Analytics Engineer

About Landbot

Operating in more than 80 countries, Landbot offers a platform that helps companies to create unbeatable chatbot conversations in different channels : Web, WhatsApp, and Messenger.

At Landbot , we’re building a high-performance team that blends engineering excellence, product mindset, and customer obsession . We believe quality and speed go hand in hand — and we’re looking for a Lead Data Analytics Engineer to help us 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.

How You Will Make a Difference

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, not on 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 their daily decisions.

You have proven experience 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.

You’re fluent in business metrics and know how to translate data into decisions that impact ARR, retention, and growth.

You are storytelling-driven : able to turn complex data into clear, persuasive insights and visualizations.

You value productivity : you focus on the 20% of insights that deliver 80% of the value.

You are comfortable working with experiments (A / B tests, hypothesis validation) and enjoy bridging the gap between product discovery and data.

You thrive in a cross-functional environment , collaborating with engineers, PMs, and business teams.

You care about governance and trust in data , ensuring consistency and quality in everything you build.

You are curious, growth-oriented, and passionate about building the foundation for AI-ready data products .

Proficient with Git / GitLab, applying best practices in version control and GitOps workflows for reproducible and collaborative development.

Excellent communication skills in English & Spanish.

We require eligibility to work in Spain and are also open to collaboration agreements.

Hiring Process

HR Interview – 20 min

Team Interview – 60 min

Interview with Leadership - 20 min

CEO - 15 min

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!