Analytics Engineer - Especialista I (Diretoria de Dados) · [CONFIDENCIAL]

BrazilRemoteFull-timemid

Data EngineerData

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Overview

This position is listed on behalf of a partner company, which manages all applications and next steps.

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Data EngineerData

We are seeking an experienced Analytics Engineer to shape reliable, scalable, and well-governed data foundations that support strategic business decisions. In this specialist-level role, you will transform complex business requirements into high-quality data models, trusted metrics, and actionable insights. You will work across data engineering, analytics, and business teams, helping ensure data is accurate, accessible, secure, and cost-efficient. The role combines hands-on technical expertise with end-to-end ownership, from stakeholder discovery and solution design to production delivery. You will also help establish engineering best practices, strengthen data quality, and empower stakeholders through self-service analytics. This is an opportunity to influence data architecture, mentor colleagues, and drive meaningful business outcomes in a collaborative, technology-driven environment.

Lead data modeling and transformation: Design, build, and maintain robust data layers in Google BigQuery, translating complex business rules into clean, efficient, scalable, and version-controlled code.
Conduct exploratory data analysis: Develop ad hoc analyses to investigate business and data-related issues, identify root causes, prevent data-related impacts on Data Science models, and generate actionable insights.
Ensure data quality and governance: Implement automated data tests, quality monitoring, and technical documentation to maintain the reliability of critical business indicators and support compliance with Brazil’s General Data Protection Law (LGPD).
Optimize performance and costs: Improve query performance and data pipelines, ensuring scalability for large data volumes while managing cloud processing costs effectively.
Partner with business and product teams: Collaborate with Product Managers and business stakeholders to understand complex challenges, lead end-to-end discovery, and propose proactive architectural solutions, including semantic layers in Looker.
Promote data-driven decision-making: Support self-service Business Intelligence (BI) by building accessible metrics and analytical layers that enable stakeholders to explore data and make informed decisions.
Provide technical leadership and mentorship: Serve as a technical reference for the team through code reviews, pair programming, knowledge sharing, and the promotion of engineering best practices.
Own delivery from discovery to production: Take responsibility for the full development lifecycle, including requirements gathering, critical analysis, solution design, documentation, implementation, validation, and production deployment.
Challenge assumptions and solve root causes: Go beyond initial requests to identify underlying business problems, question assumptions, and deliver sustainable solutions rather than short-term fixes.

Requirements

~2 min read

End-to-end ownership and analytical thinking: Demonstrated ability to work autonomously and consultatively, leading initiatives from stakeholder discovery and technical refinement through documentation, development, validation, and production delivery.
Advanced SQL and Google BigQuery: Strong practical experience transforming and querying data in BigQuery, with a solid understanding of query optimization, processing efficiency, cost management, and scalable data solutions.
Python for data engineering: Proficiency in Python, particularly for developing and maintaining stored procedures (PROCs), alongside its application in exploratory data analysis and data problem-solving.
Data modeling and semantic layers: Extensive experience refining source data, designing reliable data models, and building semantic layers that support business analysis, competitive insights, and the evaluation of analytical models.
Looker and business intelligence: Strong hands-on experience with Looker, including the development of semantic and metrics layers, interactive dashboards, and reliable reporting solutions.
Data quality, governance, and LGPD compliance: A rigorous approach to automated testing, data validation, documentation, governance, and the classification and protection of confidential and sensitive information. Experience reducing recurring data incidents is highly valuable.
Workflow orchestration: Practical experience using Apache Airflow to orchestrate and manage data pipelines and workflows.
Software engineering practices: Familiarity with Git-based version control, code reviews, collaborative development, and continuous integration and continuous delivery (CI/CD) pipelines.
Communication and collaboration: Strong communication skills and the ability to work effectively with technical teams, Product Managers, and business stakeholders, translating complex needs into practical data solutions.
Autonomy and critical thinking: A proactive, detail-oriented mindset, sound technical judgment, and the ability to challenge assumptions, investigate root causes, and manage priorities independently.