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 DataStage, Netezza, 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.
8+ years of experience in data engineering roles using Azure and Snowflake.
Strong problem-solving, communication, and collaboration skills.

Azure Lead Data Engineer