Data Engineer

Role & Responsibilities

• Design, develop, and optimize scalable ETL pipelines using Python, SQL, Snowflake, and Azure technologies.
• Build, maintain, and enhance enterprise data warehouse and lakehouse solutions with a focus on performance, scalability, and reliability.
• Develop complex SQL queries, data models, and database solutions to support business and analytics requirements.
• Implement and manage data integration workflows using tools such as Azure Data Factory (ADF), Databricks/Spark, Informatica, or SnapLogic.
• Ensure data quality, governance, monitoring, and production support for data pipelines and platforms.
• Optimize query performance and troubleshoot data-related issues in production environments.
• Collaborate with cross-functional teams to design and deliver robust data solutions.
• Support batch and real-time/streaming data processing requirements.

Preferred Candidate Profile

• 3-6 years of experience in Data Engineering, Data Warehousing, or ETL Development.
• Strong hands-on experience with Snowflake, ETL processes, Python, and Advanced SQL (mandatory).
• Experience working with Azure data services, including Azure Data Factory (ADF), Databricks, Azure Storage, and Event Hub.
• Solid understanding of data modeling, database design, query optimization, and performance tuning.
• Experience with relational databases such as Oracle, SQL Server, MySQL, and PostgreSQL.
• Exposure to CI/CD pipelines, GitHub, Azure DevOps, and DataOps practices.
• Experience with data integration tools such as Informatica or SnapLogic.
• Knowledge of batch and streaming data pipeline architectures.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to work in a fast-paced environment and support enterprise-scale data platforms.