[Remote] Data Engineer (Databricks + Python + Azure)

Descrição da Vaga

Requisitos

• Current knowledge of an using modern data tools like (Databricks, FiveTran, Data Fabric and others); Core experience with data architecture, data integrations, data warehousing, and ETL/ELT processes
• Applied experience with developing and deploying custom whl and or in session notebook scripts for custom execution across parallel executor and worker nodes
• Applied experience in SQL, Stored Procedures, and Pyspark based on area of data platform specialization
• Strong knowledge of cloud and hybrid relational database systems, such as MS SQL Server, PostgresSQL, Oracle, Azure SQL, AWS RDS, Aurora or a comparable engine
• Strong experience with batch and streaming data processing techniques and file compactization strategies
• Strong analytical and problem-solving skills
• Ability to work effectively in cross-functional and distributed teams
• Clear communication skills, with the ability to explain technical concepts to non-technical stakeholders
• Proactive mindset with a strong sense of ownership
• Commitment to delivering high-quality, reliable data solutions
• Strong hands-on experience with Databricks in Azure environments
• Advanced proficiency in Python and PySpark for distributed data processing
• Experience building and optimizing data pipelines in Azure (Azure Data Factory, Azure SQL, Data Lake Storage, etc.)
• Solid understanding of data warehousing, data lakehouse concepts, and ETL/ELT frameworks
• Experience working with relational databases such as SQL Server, PostgreSQL, Oracle, or similar
• Knowledge of batch and streaming data processing patterns
• Experience working with large, complex datasets in cloud-based distributed environments

Responsabilidades

• Design, develop, and maintain scalable data pipelines using Databricks (PySpark) and Python
• Build and optimize ETL/ELT processes within Azure cloud environments
• Implement data models following modern Data Lakehouse principles (e.g., Medallion architecture)
• Ensure data quality, consistency, and performance across ingestion, staging, and curated layers
• Collaborate with data architects, analysts, and business stakeholders to translate healthcare data requirements into technical solutions
• Develop reusable data transformation logic and modular processing components
• Support deployment processes following CI/CD and DevOps best practices
• Monitor and optimize data workflows for performance, scalability, and reliability
• Contribute to data governance, security, and compliance practices relevant to healthcare environments