Lead Analytics Engineer

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Lead Analytics Engineer based in Brazil. We are looking for an experienced Lead Analytics Engineer to provide technical leadership and shape the organization's analytics engineering strategy. In this role, you will design and maintain a reliable, scalable single source of truth that empowers business teams to make informed, data-driven decisions. You will build robust data models, develop semantic layers, and establish effective data cataloging and governance practices. You will also improve data quality, observability, and documentation while optimizing data environments for AI-powered development tools. Working closely with Data Platform, Engineering, and business stakeholders, you will define best practices, mentor fellow engineers, and bridge the gap between complex business needs and scalable technical solutions. This is an opportunity to make a meaningful impact in a fully remote, purpose-driven environment that values transparency, curiosity, collaboration, and innovation. Accountabilities - Data modeling and single source of truth: Design, build, and maintain clean, tested, and well-transformed data models within the data warehouse using dbt or Dataform, ensuring consistent and trusted business data. - Semantic layer and self-service analytics: Develop and maintain semantic layers and data catalogs, enabling Business Analysts to create dashboards and operational analyses efficiently and with minimal ambiguity. - AI-ready data context: Enrich schemas, metadata, and repository documentation to provide context for AI-powered coding tools, including Claude Code and Copilots, improving technical productivity and self-service capabilities. - Data quality and observability: Establish and implement testing frameworks, including dbt tests, monitoring alerts, and observability practices to ensure data consistency and reliability across the organization.

- Technical leadership: Define and promote development standards, DataOps practices, pull request reviews, and version control workflows. Mentor and support other Analytics Engineers in their technical development. - Cross-functional collaboration: Partner with Data Platform and Engineering teams to integrate data pipelines, establish data contracts, and ensure reliable data flows across systems. - Continuous improvement: Identify opportunities to strengthen analytics engineering processes, improve documentation, and make data assets more accessible and useful to technical and business stakeholders. Requirements - Professional experience: At least 6 years of experience working in data environments, including a minimum of 3 years specializing in Analytics Engineering. - Leadership experience: At least 2 years of experience leading Analytics Engineering teams or specialized engineering groups. - SQL and transformation tools: Advanced SQL skills and strong hands-on experience with dbt or Dataform in cloud data warehouse environments. - Cloud data platforms: Experience with one or more platforms such as Google Cloud Platform (GCP) BigQuery, Snowflake, or Amazon Redshift. - Data modeling: Practical experience with analytical data modeling methodologies, including Kimball, Star Schema, and Data Vault. - Software engineering practices: Proficiency with Git and version control, experience implementing CI/CD workflows for data through DataOps, and intermediate Python knowledge. - Data governance and cataloging: Experience implementing and maintaining data catalogs and documentation solutions such as DataHub, Atlan, or dbt Docs, as well as developing and maintaining semantic layers. - Analytical problem-solving: Ability to translate complex business requirements into scalable, reliable technical data models.