Data Engineer (Only W2)

• Design, develop, and maintain scalable data pipelines for data ingestion, transformation, and processing.
• Write complex SQL queries involving joins, CTEs, subqueries, window functions, and aggregations.
• Build and manage end-to-end ETL/ELT pipelines to process data from multiple sources.
• Debug, troubleshoot, and resolve data pipeline failures and data quality issues.
• Analyze and optimize SQL queries and pipeline performance.
• Develop data transformation workflows to convert raw data into structured, analytics-ready datasets.
• Work with Databricks to develop, execute, and optimize data processing workflows.
• Monitor pipeline performance, identify bottlenecks, and implement improvements.
• Ensure data accuracy, consistency, reliability, and integrity across data workflows.
• Collaborate with cross-functional teams to understand data requirements and deliver effective solutions.

We are looking for a skilled Data Engineer with strong hands-on experience in SQL, data pipeline development, and ETL/ELT processes. The ideal candidate should be proficient in designing, building, debugging, maintaining, and optimizing data pipelines to transform raw data into reliable, high-quality datasets for analytics and business consumption.

Candidates with hands-on experience in Databricks are highly preferred. The role requires strong problem-solving skills and the ability to independently manage data engineering tasks from data ingestion through transformation and delivery., * Strong hands-on experience in SQL and complex query development.

• Proven experience in developing and maintaining data pipelines.
• Solid understanding of ETL/ELT concepts and end-to-end data processing.
• Experience debugging, troubleshooting, and optimizing existing pipelines.
• Strong knowledge of data transformation, data integration, and data warehousing concepts.
• Experience handling large datasets and improving data processing performance.
• Strong analytical and problem-solving skills.

Preferred Skills

• Hands-on experience with Databricks.
• Experience with Apache Spark and PySpark.
• Programming experience in Python.
• Familiarity with cloud-based data platforms such as AWS, Azure, or Google Cloud Platform.
• Experience with workflow orchestration and data pipeline monitoring tools.