Lead Analytics Engineer

BrazilRemoteFull-timelead

Data EngineerData

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Quick Summary

Requirements Summary

At least 6 years of experience working in data environments, including a minimum of 3 years specializing in Analytics Engineering.

Technical Tools

Data EngineerData

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.

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

~2 min read

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.

What We Offer

~2 min read

✓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.