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).