Lead Analytics Engineer

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. Communication and collaboration: Strong interpersonal skills, with the ability to work effectively across technical and business teams, communicate complex concepts clearly, and align stakeholders around data solutions. Mentorship and ownership: Demonstrated ability to guide other engineers, establish technical standards, provide constructive feedback, and encourage continuous improvement. Location and work arrangement: The role is advertised as fully remote across Latin America. Candidates based in Brazil should confirm country-specific eligibility and any employment requirements during the application process. Benefits Fully remote work: The position is advertised as fully remote within Latin America; eligibility for candidates based in Brazil should be confirmed. Purpose-driven work: Contribute to a financial inclusion mission focused on expanding access to credit and creating meaningful value for consumers and merchants. Trust-based culture: Work in an environment that emphasizes intentional decision-making, transparency, and accountability. Creativity and curiosity: Bring forward new ideas, explore innovative approaches, and contribute to continuous improvement. Inclusive collaboration: Participate in a culture that values every team member's voice, encourages feedback, and supports a sense of belonging. Impact-focused environment: Help build reliable data foundations that support better business decisions and improve the effectiveness of analytics and AI-enabled workflows. Compensation and additional benefits: Salary, healthcare coverage, paid time off, equipment, and other specific benefits were not detailed in the original posting and should be confirmed with the hiring team.