A Data Engineer designs, builds and manages the information or big data infrastructure. Develops the architecture that helps analyze and process data in the way the organization needs it. Makes sure those systems are performing smoothly.
We are seeking an experienced Data Engineer to join our team in Chicago, IL. The ideal candidate will be responsible for designing, developing, and maintaining scalable data pipelines and modern data infrastructure to support business intelligence, analytics, and machine learning initiatives. The successful candidate will have strong expertise in ETL/ELT development, cloud platforms, SQL optimization, and large-scale data processing.
Key Responsibilities:
Design, develop, and optimize scalable data pipelines for enterprise data platforms. Build and maintain robust data ingestion frameworks from multiple structured and unstructured data sources. Develop, implement, and optimize ETL/ELT workflows for processing large-scale datasets. Design and maintain data models to support reporting, analytics, and business intelligence. Monitor data pipeline performance and troubleshoot production issues. Ensure data accuracy, integrity, security, and governance across all data platforms. Optimize SQL queries and database performance for high-volume workloads. Implement data quality checks, validation frameworks, and monitoring processes. Collaborate with data scientists, analysts, software engineers, and business stakeholders to deliver reliable data solutions. Support cloud-based data engineering initiatives and continuously improve data processing performance.
Required Skills:
Strong experience with Data Engineering concepts and best practices. Expertise in ETL/ELT pipeline development. Strong SQL programming and query optimization skills. Experience with cloud platforms such as Azure, AWS, or Google Cloud Platform. Hands-on experience building scalable data pipelines. Knowledge of data modeling and data warehousing concepts. Experience with data integration from multiple data sources. Strong troubleshooting and performance tuning skills. Familiarity with data governance, security, and data quality frameworks.
Preferred Skills:
Experience with PySpark, Spark, or distributed data processing frameworks. Experience with Azure Data Factory (ADF), Databricks, or similar ETL tools. Exposure to machine learning data pipelines. Knowledge of DevOps and CI/CD for data engineering.
