Senior Data Engineering Manager
New
BrazilFull-TimeManager
Salary not disclosed
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Job Details
- Languages
- Portuguese and English
- Required Skills
- SQLCloud ComputingPeople ManagementData engineering
Requirements
- Strong technical background in data engineering and analytics, with proven experience designing and scaling data solutions in complex environments.
- Deep knowledge of SQL, dimensional modeling, data transformation frameworks, cloud-based data platforms, and orchestration architectures.
- Strong business acumen with the ability to translate commercial, financial, and operational contexts into analytical frameworks and decisions.
- Advanced analytics understanding, including experimentation (A/B testing), causal analysis, predictive modeling, and segmentation concepts.
- Experience leading large technical or analytical organizations, including responsibility for managers, hiring, and talent development.
- Strong understanding of modern data platforms, including data contracts, semantic layers, governance, observability, and self-service models.
- Excellent executive communication skills in both Portuguese and English, with the ability to move between technical depth and strategic storytelling.
- Experience working in complex, multi-domain organizations with competing priorities and large-scale data ecosystems.
Responsibilities
- Define and lead the overall data vision, ensuring alignment between data strategy, architecture, and business outcomes across the organization.
- Guide platform and architecture decisions with a strong focus on impact, scalability, and long-term value rather than tooling preferences.
- Act as a strategic bridge between data teams and business leadership (Commercial, Operations, Finance, Product), translating business needs into analytical solutions.
- Lead and scale a large data organization, managing multiple managers, performance calibration, succession planning, and organizational design decisions.
- Drive the democratization of data through AI-powered tools, enabling self-service analytics and conversational access to metrics and insights.
- Operate the data function as a product, defining OKRs, KPIs, and health metrics such as reliability, adoption, cost, and user satisfaction.
- Elevate technical and analytical standards across the organization by shaping hiring, capability development, and seniority calibration.
- Partner with leadership to define the future expansion of data capabilities, including Data Science, Data Analytics, and Business Analytics.
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