Senior Data Architect

FCamara Consulting & Training

Join FCamara as a Senior Data Architect. Develop modern data architectures and enhance existing platforms for a leading tech company in Brazil.

Posted 7/30/2026full-timeRemote • 🇧🇷 BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in Data Engineering and Data Architecture, with a strong focus on modern data platforms, data quality frameworks, and governance processes. Proficient in building scalable data pipelines and optimizing performance for AI and Analytics initiatives.

Highest-signal resume keywords

Data EngineeringData ArchitectureLakehouse ArchitecturesApache SparkData Quality Frameworks

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Data ModelingELT ProcessesBatch ProcessingStreaming PipelinesData GovernanceMetadata ManagementData LineageFeature EngineeringPerformance OptimizationData Security

Soft Skills

Technical LeadershipCollaborationMultidisciplinary Teamwork

Tools & Technologies

AzureData MeshAI-ready Data PlatformsVersion ControlAutomated Testing

Industry Keywords

Data as a ProductData QualityData CatalogData DiscoverySemantic Layers

Tech Stack

Tools & technologies

ApacheAzureCloudSparkVault

About the role

Key responsibilities & impact

Design, implement and evolve modern data architectures (Lakehouse and Data Platforms).
Lead initiatives to modernize legacy data platforms to cloud environments (Azure, multi-cloud or hybrid).
Develop and maintain scalable and resilient data pipelines using ELT processes, batch, streaming and near real-time.
Implement and manage Bronze, Silver and Gold data layers.
Define and enforce data quality frameworks, including validation, monitoring, SLAs and quality controls.
Structure data governance processes, metadata management, data catalog, data lineage and data discovery.
Model data using approaches such as Data Vault, dimensional modeling and semantic layers for Analytics and AI.
Prepare data for Machine Learning and Artificial Intelligence initiatives, including feature engineering and data preparation.
Optimize performance and costs of distributed workloads on data platforms.
Define architectural standards, data engineering best practices and technical guidelines for the team.
Implement best practices for versioning, automated testing, observability and pipeline monitoring.
Lead data platform transformation initiatives, promoting concepts such as Data Mesh, Data as a Product and AI-ready Data Platforms.
Work with technical and business areas to identify needs and turn data into strategic assets.
Ensure data security, governance and access control through security policies and RBAC.

Requirements

What you’ll need

Strong experience in Data Engineering and Data Architecture.
Advanced knowledge of Lakehouse architectures and modern data platforms.
Experience with distributed processing using Apache Spark.
Experience modernizing legacy environments to the cloud (preferably Azure; may include hybrid or multi-cloud).
Experience building and orchestrating ELT, batch and streaming pipelines.
Knowledge in data modeling (Data Vault, dimensional and semantic).
Experience with data quality frameworks, governance, data catalog, metadata and data lineage.
Knowledge of data integration for Analytics, Machine Learning and Artificial Intelligence projects.
Experience optimizing performance and costs in distributed environments.
Familiarity with modern software engineering practices, including version control, automated testing, observability and monitoring.
Knowledge of data security, access control (RBAC) and governance policies.
Technical leadership ability, setting standards and evolving data platform maturity.
Ability to work in multidisciplinary environments, connecting technical teams and business areas.
Experience with product-oriented data platforms (Data as a Product).
Experience implementing Data Mesh.
Knowledge of AI-ready Data Platforms.
Experience building datasets and semantic layers for AI and Analytics.
End-to-end view of the data journey, from ingestion to consumption by AI applications.
Strong focus on Data Quality as a pillar for AI model reliability.
Practical experience with modern governance and metadata management frameworks.
Experience in technical leadership of Data Engineering teams.

Benefits

Comp & perks

Not specified 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score