Empresa: remopt
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