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.

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.

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;

* Fluent english.

Modelo de contratação:

* PJ.

Forma de atuação:

* Híbrido (3x por semana presencial no escritório de Pinheiros/SP).