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Azure Databricks Data Engineer
Location
Argentina
Posted
123 days ago
Salary
0
Seniority
Mid Level
Job Description
Azure Databricks Data Engineer
OZ
• Design and implement end-to-end data solutions on the Azure platform, including data ingestion, data processing, data storage, and data visualization. • Develop and maintain data pipelines using Azure Data Factory, Azure Databricks, Azure Data Lake Storage, and other relevant tools and technologies. • Collaborate with data architects and data scientists to understand data requirements and design scalable and optimized data models and schemas. • Implement data integration solutions to extract, transform, and load (ETL) data from various sources into Azure data platforms. • Ensure the reliability, availability, and performance of data solutions by monitoring and optimizing data pipelines and storage systems. • Troubleshoot and resolve data-related issues, including data quality, performance, and security concerns. • Collaborate with cross-functional teams to gather business requirements and translate them into technical solutions. • Stay updated with the latest trends and advancements in Azure data technologies and provide recommendations for adopting new tools and techniques. • Perform data profiling, data validation, and data cleansing activities to ensure data accuracy and consistency. • Document technical specifications, data flows, and processes for reference and knowledge sharing.
Job Requirements
- 2+ years of proven work experience with Azure data integration services, Data Modeling, and Data Architecture.
- Proven experience as a Data Engineer with a focus on Azure cloud technologies.
- Strong knowledge of Azure data services, including Azure Data Factory, Azure Databricks, Azure Data Lake Storage, Azure SQL Database, and Azure Synapse Analytics.
- Proficient in programming languages such as Python, SQL, and PowerShell for data manipulation and automation.
- Experience with data modeling and designing efficient data structures for analytics and reporting purposes.
- Solid understanding of data integration techniques, including ETL processes and data transformation.
- Familiarity with big data technologies like Apache Spark and Hadoop is a plus.
- Strong problem-solving skills and the ability to debug and resolve complex data issues.
- Excellent communication and collaboration skills to work effectively with cross-functional teams.
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• Analyse and maintain **RDF/TTL data models** and vocabularies; • Develop, optimise, and maintain **SPARQL queries;** • Support data ingestion, transformation, and validation workflows; • Ensure consistency and correctness of semantic data across the platform; • Collaborate with backend engineers to integrate semantic logic into application flows; • Assist in documenting semantic models, assumptions, and constraints; • Participate in troubleshooting data quality and reasoning issues.
