Data Engineer II

Responsibilities

• Design, develop, and maintain scalable ELT/ETL pipelines using Databricks, Apache Spark, SQL, and Python
• Build and maintain reliable, high-quality datasets that support enterprise analytics, operational reporting, and AI use cases
• Develop data transformations using Delta Lake and Lakehouse architecture best practices
• Integrate data from enterprise applications, APIs, databases, files, and other internal and external sources
• Collaborate with business stakeholders, analysts, and product teams to understand data requirements and deliver scalable solutions
• Monitor, troubleshoot, and optimize production data pipelines for reliability, scalability, and performance
• Participate in migrating and modernizing legacy data processes to cloud-native architectures
• Improve data quality through validation, testing, monitoring, and automation
• Contribute to engineering best practices through code reviews, documentation, and knowledge sharing
• Support continuous improvement of the enterprise data platform by identifying opportunities for automation, optimization, and simplification

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience
• 3+ years of experience in Data Engineering or a related technical role
• Strong SQL development skills
• Strong Python programming experience
• Experience building data pipelines using Apache Spark and Databricks (or similar distributed data processing platforms)
• Experience developing modern ETL/ELT pipelines
• Understanding of data modeling and data warehousing concepts
• Experience working with cloud-based data platforms, preferably AWS
• Familiarity with Delta Lake or similar modern data storage technologies
• Experience using Git and collaborative software development practices
• Strong analytical, troubleshooting, and problem-solving skills
• Excellent communication and collaboration skills

Demonstrates expertise in designing and developing scalable ELT/ETL pipelines using Databricks, Apache Spark, SQL, and Python, while ensuring data quality and optimizing performance in cloud-based environments. Collaborates effectively with stakeholders to deliver data solutions that support enterprise analytics and operational reporting.