Azure Data Engineer

Rapsys Technologies Pte Ltd.; Bengaluru Urban; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Rapsys Technologies Pte Ltd. in Bengaluru, India, is hiring an Azure Data Engineer to design, develop and optimize data solutions using Azure Databricks, PySpark, SQL and modern ETL/ELT pipelines. The role focuses on data warehousing, data modeling, and scalable architectures to support business analytics.

The ideal candidate will have hands-on experience with Azure data services, data quality, and performance tuning; the position is office-based in Bangalore Urban.

Qualifications

• Experience with Azure data services and cloud-based data architectures.
• Strong skills in designing and optimizing ETL/ELT pipelines.
• Proficiency in data modeling and data warehousing concepts.

Responsibilities

• Design, develop, and optimize data solutions in Azure Databricks.
• Implement robust ETL/ELT pipelines and data quality checks.
• Model data for analytical solutions and support business analytics.

Skills

Azure Databricks

SQL

PySpark

ETL/ELT pipelines

Data modeling

Job description

We're Hiring: Azure Data Engineer!

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.

Location:

Bangalore Urban, India

Work Mode:

Work from Office

Role:

Azure Data Engineer

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 optimization, 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 modeling techniques for analytical solutions.
• PySpark: Advanced proficiency in PySpark for developing highly optimized and scalable data processing applications, including performance tuning, distributed computing concepts, and large-scale data transformations.

Ready to shape the future of data?