Azure Data Engineer

Work Mode: Hybrid

Key Responsibilities

• Design and develop ETL/ELT pipelines using Azure Data Factory, Snowflake,

and DBT.

• Build and maintain data integration workflows from various data sources to

Snowflake.

• Write efficient and optimized SQL queries for data extraction and transformation.
• Work with stakeholders to understand business requirements and translate them

into technical solutions.

• Monitor, troubleshoot, and optimize data pipelines for performance and

reliability.

• Maintain and enforce data quality, governance, and documentation standards.
• Collaborate with data analysts, architects, and DevOps teams in a cloud-native

environment.

Must-Have Skills

• Strong experience with Azure Cloud Platform services.
• Proven expertise in Azure Data Factory (ADF) for orchestrating and automating

data pipelines.

• Proficiency in SQL for data analysis and transformation.
• Hands-on experience with Snowflake and SnowSQL for data warehousing.
• Practical knowledge of DBT (Data Build Tool) for transforming data in the

warehouse.

• Experience working in cloud-based data environments with large-scale datasets.

Good-to-Have Skills

• Experience with Azure Data Lake, Azure Synapse, or Azure Functions.
• Familiarity with Python or PySpark for custom data transformations.
• Understanding of CI/CD pipelines and DevOps for data workflows.
• Exposure to data governance, metadata management, or data catalog tools.
• Knowledge of business intelligence tools (e.g., Power BI, Tableau) is a plus.

Qualifications

• Bachelor’s or master’s degree in computer science, Data Engineering,

Information Systems, or a related field.

• 4+ years of experience in data engineering roles using Azure and Snowflake.

Key Skills: Azure, Snowflake, SQL, Data Factory, DBT

Key Responsibilities

• Design and develop ETL/ELT pipelines using Azure Data Factory, Snowflake,

and DBT.

• Build and maintain data integration workflows from various data sources to

Snowflake.

• Write efficient and optimized SQL queries for data extraction and transformation.
• Work with stakeholders to understand business requirements and translate them

into technical solutions.

• Monitor, troubleshoot, and optimize data pipelines for performance and

reliability.

• Maintain and enforce data quality, governance, and documentation standards.
• Collaborate with data analysts, architects, and DevOps teams in a cloud-native

environment.

Bachelor’s or master’s degree in computer science, Data Engineering,

Information Systems, or a related field.