Senior Data Engineer

W2 candidates only, we cannot consider C2C or 1099 for this opportunity. Candidates should be eligible to work for any employer in the United States without needing Visa sponsorship Position Summary We are seeking a highly skilled Data Engineer with 7+ years of experience designing, developing, and supporting enterprise-scale data platforms. This role will support a strategic initiative to migrate a legacy Netezza environment to Databricks, enabling next-generation analytics and data-driven decision making across customer-facing retail operations. The successful candidate will play a critical role in modernizing a highly visible and business-critical customer data platform that supports operations. The ideal candidate brings deep expertise in Databricks, PySpark, data engineering best practices, and cloud-based data architecture, along with experience building scalable ingestion, transformation, testing, and monitoring solutions. Key Responsibilities Provide support to the migration of legacy data assets from Netezza to Databricks. Design, develop, and optimize scalable data pipelines using PySpark. Build and support batch and streaming ingestion frameworks for enterprise data processing. Implement data transformation and conversion strategies to support platform modernization initiatives. Develop and maintain data solutions utilizing Databricks Lakehouse architecture and Medallion design patterns. Establish and enforce engineering best practices, including: Coding standards Automated testing frameworks Monitoring and operational support procedures Collaborate with business stakeholders, architects, analysts, and engineering teams to deliver high-quality solutions. Perform code reviews and mentor junior team members on data engineering best practices. Troubleshoot and resolve performance, scalability, and data integrity issues. Drive continuous improvement initiatives across the data engineering ecosystem. Support integration testing, regression testing, and production deployment activities. Required Qualifications Bachelor's degree in Computer Science, Information Systems, Engineering, or related field. 7+ years of Data Engineering experience in enterprise environments. Strong hands-on experience with Databricks (required). Advanced proficiency in PySpark development. Experience developing both batch and streaming data pipelines. Strong SQL and data modeling expertise. Experience implementing data quality frameworks, validation processes, and reconciliation strategies. Experience building and maintaining automated testing and integration suites. Experience creating and supporting CI/CD pipelines for data platforms. Strong understanding of cloud-based data architectures and modern analytics platforms. Excellent communication and collaboration skills. Preferred Qualifications Experience with Netezza migrations or legacy Netezza environments. Experience with Delta Lake, Unity Catalog, and Databricks Workflows. Experience working with large-scale retail, customer, or omnichannel data platforms. Familiarity with Azure cloud services and modern data integration patterns. Experience with infrastructure-as-code and DevOps methodologies. Knowledge of data governance, lineage, and metadata management solutions. Top Priority Skills: Databricks, PySpark, Data Migration, Test Automation, CI/CD, Data Quality, Medallion Architecture, Streaming & Batch Processing #J-18808-Ljbffr