Analytics Engineering Manager, Data Platform & Governance

About the role

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Analytics Engineering Manager, Data Platform & Governance based in Brazil.

We are looking for a hands-on Analytics Engineering Manager to take ownership of data governance, quality, and platform reliability across a multi-brand business. You will define and drive the data platform roadmap, ensuring that source data, pipelines, metrics, dashboards, and AI-powered tools remain accurate, secure, and trustworthy. Working across product, marketing, operations, and finance, you will translate business needs into clear priorities and scalable governance practices. You will also lead the ongoing migration to Lightdash, strengthen the semantic layer, and establish consistent event-tracking standards. This is a build-oriented leadership role: you will begin as an individual owner, combining technical execution with strategic planning, before growing the team as the function expands. You will have the opportunity to shape the foundations, standards, and processes that enable the entire organization to make better decisions through data.

Accountabilities -

Own the data platform and governance roadmap, gathering requirements from product, marketing, operations, and finance, prioritizing initiatives, and balancing stakeholder needs with platform reliability and long-term scalability.

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Establish automated data quality and freshness monitoring across source systems, pipelines, warehouse models, and reports, identifying upstream changes before they cause downstream issues.

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Manage data incidents through resolution, improving detection, prevention, and response processes.

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Build and maintain end-to-end data lineage and impact analysis, connecting production sources, warehouse models, dashboards, and event tracking to assess the consequences of changes before they are deployed.

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Partner with engineering teams to introduce data contracts and integrate breaking-change detection into development and release workflows.

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Lead the migration to Lightdash, defining workspace structure, permissions, naming conventions, certification standards, and governance practices that enable reliable self-service analytics.

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Extend and maintain the semantic layer, ensuring consistent definitions for business-critical metrics and preventing conflicting or uncontrolled metric growth.

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Govern the Segment event catalog, reviewing new events, maintaining naming standards and property definitions, and implementing automated drift detection to keep tracking aligned with production.

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Ensure data accessed by AI agents and internal AI tools is documented, consistent, secure, and suitable for reliable automated querying.

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Oversee data security and privacy practices, including access controls, personally identifiable information (PII) handling, retention policies, and periodic reviews of access granted to employees and AI tools.