Azure Data Engineer

RAPSYS TECHNOLOGIES PTE LTD; Bengaluru; Presencial; INR 1.400.000 - 2.400.000; Jornada completa

Descripción de la vacante

RAPSYS TECHNOLOGIES PTE LTD in Bengaluru is seeking an experienced Azure Data Engineer to design, build and optimise data solutions using Azure Databricks, PySpark and SQL. You will shape data architecture to support analytics and decision-making across business units.

The role focuses on scalable ETL/ELT pipelines, data warehousing strategies, governance and performance tuning, with opportunities to work on cutting-edge cloud data platforms.

Formación

• Experience with Azure data services and cloud-based data architectures.
• Strong knowledge of ETL/ELT pipelines, data quality, and orchestration.
• Hands-on experience with data warehousing methodologies and analytics modeling.

Responsabilidades

• Design, develop, and optimise data solutions in Azure Databricks with scalability in mind.
• Write advanced SQL for complex queries, data modelling, and integration across systems.
• Architect and maintain robust ETL/ELT pipelines with focus on data quality and reliability.
• Implement data warehousing strategies and lakehouse architectures for analytics.
• Develop PySpark data processing applications with performance tuning.

Conocimientos

Azure Databricks

SQL

ETL/ELT Pipelines

Data Warehousing

PySpark

Descripción del empleo

We are looking for an experienced and skilled Azure Data Engineer to join our team and drive impactful data solutions. The ideal candidate will have a strong background in Azure data services, ETL processes, and cloud-based architectures to support business analytics and decision-making.

What You’ll Do:

• Azure Databricks: Expert-level proficiency in designing, developing, optimising, and managing scalable data solutions within Azure Databricks environments, including performance tuning and architectural best practices.
• SQL: Mastery of advanced SQL for complex query optimisation, database design, data modelling, and efficient data manipulation across various database systems.
• ETL/ELT Processes: Extensive experience in architecting, implementing, and maintaining robust, scalable, and efficient ETL/ELT pipelines, with a focus on data orchestration, error handling, and data quality frameworks.
• Data Warehousing (DWH): Deep understanding and practical experience with various Data Warehousing methodologies, lakehouse architectures, and advanced data modelling techniques for analytical solutions.
• PySpark: Advanced proficiency in PySpark for developing highly optimised and scalable data processing applications, including performance tuning, distributed computing concepts, and large-scale data transformations.

Ready to shape the future of data?