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.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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