Analytics Engineer (Data)

Description

Job Summary: This Analytics Engineer will play a central role in building and evolving the Common Data Model, ensuring that business rules and strategic requirements are translated into consistent, easily consumable data models. Key Highlights: 1. Model data with excellence across robust analytical layers. 2. Collaborate with business areas to deliver actionable analytics. 3. Ensure data governance and data quality. Description: Technical Skills: * Practical experience in data modeling and building analytical layers. * Proficiency in dbt for data model transformation and versioning. * Strong SQL skills for writing optimized queries and reusable models. * Knowledge of Python for automating data processes. * Experience with Data Warehousing. * Experience in data governance: documentation, cataloging, and data contracts. * Experience with visualization tools (Metabase, Looker, Power BI). Soft Skills: * Strong communication skills to translate business rules into clear data models. * Proactivity to identify and address incidents before they cause impact. * Collaboration with business areas to transform requirements into actionable analytics. * Ownership mindset regarding data quality and consistency. Bonus Points If You Have: * Hands-on experience in data observability: data quality testing, pipeline monitoring, reliability metrics. * Knowledge of DataOps practices. As an Analytics Engineer on Gabriel’s Data team, you will play a central role in building and evolving our Common Data Model, ensuring that business rules and strategic requirements are translated into consistent, easily consumable data models for the entire company. Your responsibilities will include: * Modeling data with excellence: transforming raw data into robust analytical layers, designing entities and reports that enable fast, secure, and reusable analysis. * Translating business into data: collaborating closely with business areas to understand their needs and converting them into clear, actionable data solutions. * Ensuring governance: implementing and maintaining documentation, cataloging, and data contract practices to guarantee consistency and alignment across the entire data ecosystem. * Monitoring and responding to incidents: tracking the health of data pipelines and processes, proactively addressing failures to maintain analytical environment reliability. * Ensuring quality: defining and applying quality checks, tests, and observability metrics so data remains always accurate and trustworthy. * Supporting strategic analytics: assisting stakeholders in translating requirements into efficient analyses that generate insights and guide business decisions. * Collaborating on a data culture: promoting best practices for data usage, dissemination, and standardization across the company. 2512040202181901358