Vaga Para Pessoa Jurídica: Senior Data Engineer (Databricks & Ai-Enabled Data Platforms)

Keep Simple; São Paulo; Presencial; BRL 250.000 - 420.000; Jornada completa

Descripción de la vacante

KEEP SIMPLE is seeking a Senior Data Engineer to design and operate modern data platforms that enable analytics, AI/ML, and BI across the enterprise. You will lead data engineering efforts, build scalable pipelines with Databricks and cloud technologies, and set engineering standards.

You will mentor engineers, drive governance, and partner with security and infrastructure teams to ensure reliable, secure data services in Azure and AWS.

Responsabilidades

• Design, develop, and maintain scalable data pipelines using Databricks, Spark, and cloud-native tech.
• Build and optimize batch and real-time data ingestion frameworks; develop reusable patterns and accelerators.
• Implement data quality, observability, lineage, and governance across the data ecosystem.
• Design Delta Lake architectures and Medallion data models; optimize ETL/ELT pipelines.
• Design cloud-native data architectures across Azure and AWS; ensure security, reliability, and DR.
• Lead technical initiatives, mentor engineers, and drive automation and platform engineering.

Herramientas

Databricks

Spark

Delta Lake

Delta Live Tables

Unity Catalog

CI/CD

Infrastructure-as-Code

Azure

AWS

SQL Server

Oracle

Descripción del empleo

We are seeking a highly skilled Senior Data Engineer to design, build, and optimize modern data platforms that enable advanced analytics, AI/ML, and business intelligence solutions. The ideal candidate will possess deep expertise in Databricks, cloud-based data engineering, data architecture, and modern software engineering practices. This role will provide technical leadership, establish engineering standards, and drive the development of scalable, secure, and reliable data solutions supporting enterprise-wide initiatives.

Key Responsibilities

• Data Platform Engineering:Design, develop, and maintain scalable data pipelines using Databricks, Spark, and cloud-native technologies. Build and optimize batch and real-time data ingestion frameworks. Develop reusable engineering patterns, frameworks, and accelerators for enterprise data solutions. Implement data quality, observability, lineage, and governance capabilities across the data ecosystem. Support large-scale data modernization and cloud migration initiatives.
• Databricks Engineering:Design and implement Delta Lake architectures and Medallion data models. Build optimized ETL/ELT pipelines using Databricks notebooks, workflows, and Delta Live Tables. Establish performance tuning, cost optimization, and workload management best practices. Leverage Unity Catalog for data governance, security, and metadata management. Implement CI/CD pipelines and Infrastructure-as-Code for Databricks deployments.
• Cloud Data Engineering:Design cloud-native data architectures across Azure and AWS environments. Integrate enterprise data sources including SQL Server, Oracle, APIs, SaaS platforms, and streaming platforms. Build resilient and secure data services supporting high availability and disaster recovery requirements. Partner with infrastructure and security teams to implement cloud security controls and compliance standards.
• AI and Advanced Analytics Enablement:Build and optimize data products supporting AI, machine learning, and generative AI workloads. Develop feature engineering pipelines and curated datasets for model training and inference. Support vector databases, semantic search, RAG (Retrieval-Augmented Generation), and LLM-powered applications. Collaborate with Data Scientists and AI Engineers to operationalize machine learning solutions. Implement MLOps and DataOps practices to support scalable AI delivery.
• Technical Leadership:Serve as a technical lead for complex data engineering initiatives. Define architecture standards, engineering best practices, and development guidelines. Mentor junior and mid-level engineers. Conduct architecture reviews and provide technical recommendations. Drive adoption of automation, observability, and platform engineering principles.