Data Engineer

Overview This is a Full-Time position and will require 3 days onsite We are unable to provide Sponsorship or Visa Transfer at this time. We are seeking a skilled Data Engineer to design, develop, and maintain scalable batch-based data pipelines and external system integrations. This role will focus on building reliable data exchange solutions between enterprise platforms, third-party vendors, and internal business systems while supporting automation, analytics, and operational efficiency initiatives. Responsibilities Develop and optimize scalable Azure Synapse data pipelines and notebook-driven transformations using PySpark and Spark SQL Build and maintain integrations with REST APIs, SFTP processes, external vendors, and enterprise data sources Manage workflow orchestration using Azure Logic Apps, including scheduling, monitoring, retries, and alerting mechanisms Implement data quality checks, observability frameworks, and monitoring solutions to ensure high availability and SLA compliance Design secure data exchange solutions leveraging OAuth, Azure Key Vault, and enterprise authentication standards Partner with Engineering, Business Intelligence, Product, and business stakeholders to deliver scalable integration and automation solutions Troubleshoot and improve distributed data workflows for performance, reliability, and maintainability Qualifications 4–7+ years of experience in Data Engineering, Integration Engineering, or related technical roles Strong hands-on expertise with Azure Synapse Analytics, Spark/Python, SQL, and Azure Logic Apps Proven experience developing scalable batch-processing pipelines and external data integrations Strong analytical, troubleshooting, and problem-solving skills within distributed data environments Experience working in Agile development environments and collaborating with cross-functional teams Preferred Qualifications Experience with Azure Data Lake, Delta Lake, and Parquet file formats Familiarity with Azure API Management (APIM) Understanding of data governance, lineage, and metadata management practices #J-18808-Ljbffr