ABOUT THE ROLE
We are looking for an experienced Senior Data Engineer to design and operate enterprise-grade data infrastructure — including warehouses, lakes, and marts — while ensuring data quality, governance, and compliance. You will bring deep expertise in data architecture and modeling, combined with proficiency in pipeline orchestration and analytics tooling.
KEY RESPONSIBILITIES
Data Architecture & Modeling
• Design enterprise data models using dimensional modeling, Data Vault 2.0, or similar methodologies
• Architect and maintain data warehouses, data lakes, and lakehouse environments
• Define and enforce data standards, naming conventions, and schema governance across domains
Data Governance & Quality
• Implement data governance frameworks including cataloging, lineage tracking, and metadata management
• Build automated data quality checks, validation rules, and anomaly detection into pipelines
• Ensure compliance with data privacy regulations through access controls and data classification
• Maintain master data management standards, data dictionaries, and business glossaries
Pipeline Engineering & Platform
• Build and maintain batch and streaming data pipelines with full observability and alerting
• Implement change data capture patterns, real-time ingestion, and ELT/ETL frameworks
• Administer and scale cloud data platforms; optimize storage, compute, and cost efficiency
• Manage data infrastructure using infrastructure-as-code practices
Collaboration
• Partner with analytics engineers, data scientists, and BI teams to deliver trusted data products
• Define data contracts between producers and consumers; mentor junior engineers
Requisitos:
REQUIRED QUALIFICATIONS
• 5+ years of data engineering experience with a focus on enterprise data platforms
• Expert SQL skills; strong Python for data processing and automation
• Deep experience with cloud data warehouse platforms
• Hands-on experience with data transformation frameworks and workflow orchestration tools
• Experience with data governance and cataloging platforms
• Solid understanding of data quality frameworks, privacy regulations, and CI/CD for pipelines
NICE TO HAVE
AI & Machine Learning
• Experience building and maintaining feature stores to serve ML models in production
• Familiarity with vector databases and embedding pipelines for retrieval-augmented generation
• Exposure to LLM application frameworks and AI orchestration workflows
• Data lineage and audit trails applied to AI/ML workflows for compliance and reproducibility
Telecom & RAN
• Understanding of RAN architecture, including network nodes, interfaces, and data flows across 4G/5G environments
• Familiarity with telecom data sources such as performance counters, KPIs, alarm feeds, and network event logs
• Experience with high-volume, time-series network data and applying data engineering principles to telecom datasets
• Ability to collaborate with network engineers and RAN teams to define data requirements and support analytics use cases
Cloud Data Platforms
• Hands-on experience with Snowflake, including performance optimization, cost management, secure data sharing, and integration with modern ELT frameworks
Benefícios
Á combinar.