Data Analytics & Insights Specialist

Role Overview

• Design and execute advanced analyses on user digital behavior, campaign performance, and conversion funnels to identify patterns, opportunities, and risks.
• Generate actionable insights that answer strategic business questions and translate into concrete recommendations for Marketing, Product, and Commercial teams.
• Build data storytelling narratives that connect analytical findings to business decisions, tailoring the message to technical, managerial, and executive audiences.
• Define KPIs, measurement frameworks, and attribution models that holistically evaluate the performance of the digital ecosystem.
• Build and maintain robust data pipelines in BigQuery from sources such as GA4, CRM, media platforms, and transactional databases.
• Design efficient, optimized SQL queries on large-scale tables, including Google Analytics 4 export tables (events_*).
• Collaborate with Data Engineering teams on data modeling, quality, governance, and availability across GCP and AWS environments.
• Design and maintain executive dashboards in Looker Studio (or other BI tools) that deliver clear, timely, and actionable information to stakeholders.
• Present findings to C-level audiences, translating complex technical analyses into simple, actionable, business-oriented messages.
• Document methodologies, assumptions, and analytical limitations to ensure transparency and reproducibility.
• Act as a technical reference within the Analytics & Insights team, supporting junior analysts on best practices in SQL, Python, and modeling.
• Collaborate with regional teams across multiple markets, understanding local nuances and consolidating learnings at a regional level.
• Promote a data-driven culture, advocating for the responsible use of data and experimentation as a driver of decision-making.

Requirements

• Bachelor's or Master's degree in Statistics, Data Science, Engineering, Economics, Mathematics, Industrial Engineering, or related fields.
• Relevant certifications are a plus: Google Analytics, Google Cloud (Data Engineer / Data Analyst), AWS (Data Analytics), or equivalents.
• Experience in advanced analytics, data science, or business intelligence roles, preferably in digital, telecommunications, e-commerce, or media industries.
• Demonstrated experience leading end-to-end analytics projects: from problem definition to implementation and communication of results.
• Experience in regional or multinational environments is a strong plus.
• Google Analytics 4 (GA4): configuration, implementation, advanced analysis, and BigQuery export (events_*, intraday tables, event parameters, attribution).
• BigQuery / Advanced SQL: designing efficient queries, window functions, partitioning, cost optimization, and integration with GA4 and transactional data.
• AWS: hands-on experience with data services such as S3, Athena, Glue, Redshift, or equivalents.
• Python: data manipulation (pandas, numpy), visualization (matplotlib, seaborn, plotly), and modeling (scikit-learn, statsmodels).
• Machine Learning: command of supervised and unsupervised algorithms (regression, classification, clustering), model validation, performance metrics, and experimentation.
• Digital Marketing: deep understanding of the digital ecosystem (paid media, SEO, CRM, CRO, attribution, conversion funnels, customer journey).
• Data Visualization: Looker Studio, Power BI, Tableau, or similar tools.
• Experience with dbt, Airflow, or other orchestration and analytics modeling tools.
• Knowledge of experimentation platforms, CRO tools (Microsoft Clarity, Hotjar, Optimizely), and behavior analytics.
• Familiarity with causal inference frameworks (CausalImpact, DiD, propensity scoring).
• Experience with generative AI tools applied to analytics and productivity.
• Analytical & critical thinking: ability to break down complex problems and draw rigorous conclusions from data.
• Insight generation: ability to go beyond the metric and deliver the why and the so-what.
• Data storytelling: ability to tell stories with data, structuring clear and persuasive narratives for different audiences.
• Business orientation: understanding of the commercial impact of analyses and focus on generating measurable value.
• Effective communication: ability to explain technical concepts simply to non-technical audiences.
• Autonomy & proactivity: ability to identify analytical opportunities without detailed instructions.
• Collaboration: effective work with multidisciplinary, multicultural teams.
• English: intermediate to advanced (B1/B2) — able to lead meetings, presentations, and produce technical documentation in English.

Tech Stack

• Airflow
• Amazon Redshift
• AWS
• BigQuery
• Cloud
• Google Cloud Platform
• Numpy
• Pandas
• Python
• Scikit-Learn
• SQL
• Tableau

Benefits

• Proyecto de 3 meses
• Pago por honorarios