Azure Data Engineer

Tap Growth ai; Bengaluru Urban; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Tap Growth ai in Bengaluru, India is seeking an Azure Data Engineer to design and implement scalable data solutions using Azure Databricks, PySpark and SQL. The role emphasizes ETL/ELT pipelines, data warehousing, and data quality to support analytics and decision-making.

The candidate will collaborate with analytics teams to shape data architecture and drive insights through robust data models and optimized processing. This is an office-based role in Bangalore.

Qualifications

• Experience designing scalable data solutions in Azure.
• Strong command of SQL for complex queries and data modelling.
• Experience architecting robust ETL/ELT pipelines with data quality controls.
• Deep understanding of data warehousing, lakehouse concepts, and analytics.
• Proficiency in PySpark for large-scale data processing.

Responsibilities

• Design and implement Azure Databricks data solutions.
• Build and optimise ETL/ELT pipelines for reliability and scalability.
• Craft data models and warehouse schemas for analytics.
• Collaborate with analytics teams to drive data-driven decisions.
• Tune and optimise PySpark jobs for performance.

Skills

Azure Databricks

SQL

ETL/ELT

Data Warehousing

PySpark

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?