AI Data Engineer

remopt; São Paulo; Presencial; BRL 167 400 - 279 000; Tempo integral

Resumo da oferta

remopt is seeking an experienced Senior Data Engineer in São Paulo, Brazil, to design and operate enterprise-grade data infrastructure. This role involves building data models, ensuring data quality and governance, and collaborating with cross-functional teams. Ideal candidates should have over five years of experience in data engineering, strong SQL and Python skills, and hands-on knowledge of cloud data platforms like Snowflake. This position requires a focus on delivering trusted data products while maintaining compliance with privacy regulations.

Qualificações

• 5+ years of data engineering experience focused on enterprise data platforms.
• Solid understanding of data quality frameworks and privacy regulations.

Responsabilidades

• Design enterprise data models using dimensional modeling.
• Implement data governance frameworks including cataloging and metadata management.
• Build and maintain batch and streaming data pipelines.

Conhecimentos

Expert SQL skills

Strong Python for data processing

Hands-on experience with data transformation frameworks

Deep experience with cloud data warehouse platforms

Ferramentas

Snowflake

Descrição da oferta de emprego

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

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