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Brasil
Remote only
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.
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.
Establish automated data quality and freshness monitoring across source systems, pipelines, warehouse models, and reports, identifying upstream changes before they cause downstream issues.
Manage data incidents through resolution, improving detection, prevention, and response processes.
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.
Partner with engineering teams to introduce data contracts and integrate breaking-change detection into development and release workflows.
Lead the migration to Lightdash, defining workspace structure, permissions, naming conventions, certification standards, and governance practices that enable reliable self-service analytics.
Extend and maintain the semantic layer, ensuring consistent definitions for business-critical metrics and preventing conflicting or uncontrolled metric growth.
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.
Ensure data accessed by AI agents and internal AI tools is documented, consistent, secure, and suitable for reliable automated querying.
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.
Develop the documentation, ownership models, monitoring systems, and review processes needed to make governance sustainable and independent of individual contributors.
Use automation and AI-powered tools to improve quality checks, anomaly detection, lineage monitoring, and operational efficiency.
Begin as the primary owner of the function, then help recruit and develop a Lead Analytics Engineer who will report directly to you as the team grows.
Proven experience in analytics engineering, data engineering, data platform management, or a closely related discipline, with hands-on responsibility for data quality and governance.
Previous experience managing or being accountable for other people's work, including setting priorities, allocating responsibilities, and overseeing delivery.
Strong technical expertise in data warehouses and pipeline technologies, particularly Amazon Redshift, dbt, and Airflow.
Practical experience with data ingestion tools such as Fivetran and custom Airflow pipelines.
Experience managing BI platforms and governance practices at company scale, including workspace administration, permissions, reporting standards, and self-service enablement.
Familiarity with event-tracking platforms such as Segment, including event catalogs, naming conventions, property dictionaries, review processes, and tracking quality controls.
Understanding of data lineage, schema changes, impact analysis, freshness monitoring, anomaly detection, and automated data quality checks.
Ability to establish data contracts and collaborate with engineering teams to prevent breaking changes from affecting downstream systems.
Experience with semantic layers and metric governance, ensuring consistent definitions and reliable business reporting.
Strong SQL skills and the ability to investigate data issues, debug pipelines, configure platform permissions, and implement technical solutions directly.
An automation-first mindset, with experience building monitoring and quality controls rather than relying primarily on manual reviews and checklists.
Daily familiarity with AI tools such as Claude Code, Copilot, or ChatGPT for coding, automation, anomaly investigation, and documentation.
Understanding of the governance requirements associated with AI-powered data consumption, including making warehouse information safe, consistent, and understandable for AI agents.
Strong product and business orientation, with the ability to turn ambiguous stakeholder requests into a prioritized technical roadmap.
Excellent communication and stakeholder-management skills, including the ability to establish standards across teams you do not directly manage and address non-compliance constructively.
Comfortable working independently in a remote, asynchronous environment and building governance capabilities that are still evolving.
Experience with every tool listed is not required; hands-on depth in the warehouse and pipeline layer, together with credible experience maintaining BI and event-tracking standards at organizational scale, is essential.
Annual base salary of $75,000–$120,000 USD.
Fully remote working arrangement.
Flexible paid time off, with an emphasis on results and outcomes rather than hours worked.
Asynchronous collaboration and flexibility to manage your working environment.
Opportunity to shape the data governance function and its long-term roadmap.
Access to AI-powered development and analytics tools to support automation and technical delivery.
Opportunity to build and lead a growing analytics engineering team.