Senior Data Engineer (Databricks & AI Platforms) (4809)

Come work for a large global financial and insurance products company! This is your chance !!

Start a successful career in a renowned company in the international market! Great opportunity!

Global insurance and asset management company seeks a responsible, organized, dynamic and team-oriented person.

Responsabilidades e atribuições

The Senior Data Engineer is responsible for designing, building, and optimizing scalable data platforms and pipelines that support analytics, business intelligence, machine learning (ML), and AI-driven solutions. This role partners with data scientists, AI engineers, architects, and business stakeholders to deliver trusted, high-quality data products using Databricks, cloud technologies, and modern data engineering practices. The position also serves as a technical leader in enabling enterprise AI and Generative AI initiatives through robust, secure, and governed data platforms.

Key

Responsibilities

• Design, develop, and maintain scalable data pipelines and data products using Databricks, Spark, Python, and SQL
• Build and optimize batch, streaming, and real-time data integration solutions from enterprise and third-party data sources
• Implement Databricks Lakehouse architectures utilizing Delta Lake and Medallion design patterns
• Develop and maintain data products that support analytics, predictive modeling, machine learning, and Generative AI applications
• Collaborate with Data Scientists and AI Engineers to prepare, transform, and govern data for AI and ML use cases
• Design and implement feature engineering pipelines and support ML lifecycle processes
• Develop data solutions that support Retrieval Augmented Generation (RAG), vector search, semantic search, and LLM-based applications
• Optimize Spark jobs, SQL workloads, and data processing frameworks for performance, scalability, and cost efficiency
• Implement data quality, observability, lineage, governance, and monitoring capabilities
• Ensure compliance with data privacy, security, and responsible AI standards
• Contribute to CI/CD, Infrastructure-as-Code, and DataOps practices across the data platform
• Mentor junior engineers and promote engineering best practices across the organization.

Requisitos e qualificações

Required

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field
• 7+ years of experience in data engineering, ETL development, or large-scale data platform engineering
• 3+ years of hands-on experience with Databricks and Apache Spark
• Strong proficiency in Python, SQL, and distributed data processing frameworks
• Experience building cloud-based data lakes, data warehouses, and Lakehouse architectures
• Experience supporting AI, machine learning, or advanced analytics initiatives
• Strong understanding of data modeling, data governance, and enterprise data management practices
• Experience developing and optimizing large-scale data pipelines in AWS, Azure, or Google Cloud.

Preferred

Qualifications

• Experience with Databricks Delta Lake, Unity Catalog, Delta Live Tables, MLflow, Mosaic AI, and Databricks Workflows
• Hands-on experience supporting Generative AI, Large Language Models (LLMs), RAG architectures, vector databases, or AI-powered applications
• Familiarity with AI frameworks such as LangChain, Semantic Kernel, OpenAI APIs, Hugging Face, or similar technologies
• Experience with feature stores, model training pipelines, and machine learning operationalization (MLOps)
• Experience with Kafka, Event Hub, or other streaming technologies
• Databricks Certified Data Engineer Professional or equivalent cloud certification.

• Key Competencies: Databricks Platform Engineering
• Apache Spark Development
• AI & Machine Learning Data Engineering
• Generative AI Data Solutions
• Lakehouse Architecture
• Data Modeling & Data Warehousing
• Data Governance & Security
• Data Observability & Reliability Engineering
• Cloud Data Platforms (AWS/Azure)
• DataOps, CI/CD & Automation
• Technical Leadership & Mentoring.

• Success Measures: Delivery of scalable, reliable, and secure data platforms supporting analytics, AI, and business operations
• Successful enablement of AI and Generative AI use cases through high-quality, governed data products
• Improved data quality, pipeline reliability, and platform performance
• Increased automation, operational efficiency, and reuse of engineering frameworks
• Adoption of data engineering standards and best practices across development teams
• Measurable improvements in AI/ML solution delivery speed and business value realization.

Informações adicionais

Modelo de contratação: PJ.

Forma de atuação: Híbrido (3x por semana presencial no escritório de Pinheiros/SP).