Staff Data Engineer (Databricks specialist)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Engineer (Databricks Specialist) based in Brazil.

We are seeking an experienced Staff Data Engineer to lead the design and development of scalable, enterprise-grade data platforms, with a strong specialization in Databricks and modern cloud technologies. In this strategic role, you will drive complex, cross-functional data initiatives, ensuring reliable data ingestion, transformation, storage, and delivery. You will play a key role in shaping enterprise data architecture, Master Data Management (MDM), and AI-powered data capabilities. Working closely with engineering, product, and analytics leaders, you will translate business needs into innovative and scalable technical solutions. You will also champion engineering excellence, data governance, and platform performance while mentoring experienced engineers. This is an opportunity to influence technical direction, accelerate AI adoption, and build the foundations for next-generation data-driven solutions.

Accountabilities

• Own and drive the technical architecture of complex, cross-functional data initiatives, covering ingestion, transformation, storage, and serving layers.
• Design, develop, and maintain scalable, high-performance data pipelines and distributed data platforms using Databricks and cloud-native technologies across Azure, AWS, or GCP.
• Lead enterprise Master Data Management (MDM) initiatives, including golden records, entity resolution, match-and-merge processes, data domains, reference data, hierarchies, and data stewardship.
• Architect and integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, AI copilots, and orchestration frameworks, into enterprise data platforms.
• Build and maintain data foundations for machine learning and AI applications, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
• Design and deliver backend data services and APIs using REST or GraphQL, ensuring curated datasets are accessible to applications, analytics, and business intelligence platforms.
• Establish and promote engineering standards covering data modeling, quality, testing, observability, CI/CD, deployment, and responsible AI-assisted development.
• Implement enterprise-wide data governance, lineage, cataloging, and quality frameworks to ensure data consistency, reliability, and transparency.
• Lead technical design reviews, provide architectural guidance, and influence engineering decisions across multiple teams.
• Collaborate with product, analytics, and engineering leadership to define scalable data roadmaps aligned with business objectives and AI-driven opportunities.
• Identify and resolve systemic performance, reliability, and scalability challenges across the data ecosystem.
• Mentor and coach senior and mid-level engineers, promoting technical excellence and best practices in data engineering, MDM, and AI.
• Evaluate emerging technologies, frameworks, and industry trends to continuously improve platform capabilities, engineering efficiency, and resilience.

Requirements

• Bachelor's degree in Computer Science, Engineering, Management Information Systems, or a related discipline is preferred.
• 12+ years of professional experience in data engineering or software engineering, with a proven track record of architecting enterprise-scale data platforms and pipelines.
• Expert-level SQL skills and strong proficiency in Python. Experience with Scala or Java is an advantage.
• Extensive hands-on experience with Databricks and modern cloud data platforms such as Snowflake, Azure Synapse, BigQuery, or Amazon Redshift.
• Strong understanding of cloud-native architecture, distributed systems, data modeling methodologies (dimensional modeling, Data Vault, Lakehouse), and ETL/ELT architectures.
• Proven experience designing and implementing enterprise MDM solutions, including entity resolution, match-and-merge, golden records, reference data, and hierarchy management. Familiarity with Informatica, Reltio, Profisee, or similar platforms is highly valued.
• Practical experience building or integrating agentic AI systems, LLM-powered applications, RAG pipelines, and AI agent orchestration frameworks such as LangChain, AutoGen, Semantic Kernel, or MCP.
• Experience developing backend data services and REST/GraphQL APIs, with the ability to contribute across the technology stack.
• Strong knowledge of relational and NoSQL databases, as well as modern data lake and lakehouse formats such as Delta Lake, Apache Iceberg, and Parquet.
• Deep understanding of CI/CD pipelines, Infrastructure as Code (IaC), DevOps, and DataOps practices.
• Demonstrated ability to lead large-scale technical initiatives involving multiple teams and stakeholders.
• Excellent communication, collaboration, and problem-solving skills, with the ability to influence technical decisions wit