This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Engineer based in United States. This role offers an opportunity to help build and evolve a scalable, self-service data and AI ecosystem within a modern enterprise environment. You will contribute to data integration, ETL, cloud-based pipelines, and analytics solutions that support critical business needs. Working primarily with AWS and Databricks, you will develop reliable batch and near-real-time data pipelines. The position combines hands-on engineering with production support, troubleshooting, automation, and continuous improvement. You will collaborate closely with senior engineers, architects, analytics teams, infrastructure specialists, business stakeholders, and external partners. This is a strong opportunity to deepen your expertise in modern data engineering technologies while contributing to high-quality, dependable data solutions. The role is remote, with candidates in the Chicago metropolitan area preferred for occasional office collaboration. Accountabilities: - Participate throughout the Software Development Life Cycle for data integration, ETL, and cloud-based data pipeline initiatives, including requirements analysis, design, development, testing, deployment, and production support. - Design, develop, test, deploy, and support batch and near-real-time data pipelines using AWS and Databricks. - Build data transformations and integration solutions using SQL, Python, Spark, and Delta Lake. - Develop and maintain Databricks notebooks, workflows, jobs, Delta tables, and related data components. - Contribute to requirements analysis, estimation, solution design, code reviews, testing, production deployment, and technical documentation. - Create unit tests and support integration, reconciliation, performance, regression, and data quality testing.
- Monitor data pipelines, ETL processes, and cloud platform components, investigating production issues and supporting timely resolution. - Diagnose data integration and pipeline failures, participate in root cause analysis, and contribute to corrective and preventive actions. - Maintain source-to-target mappings, transformation rules, data flow diagrams, troubleshooting guides, and operational documentation. - Collaborate with senior engineers, architects, analytics teams, infrastructure teams, business stakeholders, and external partners to deliver scalable data solutions. - Support platform upgrades, deployment automation, monitoring improvements, and operational process enhancements. - Participate in a scheduled on-call rotation and provide occasional evening or weekend support for production environments and project requirements. - Stay current with AWS, Databricks, Spark, Python, SQL, Delta Lake, Unity Catalog, and emerging data engineering practices. - Identify automation, process improvement, and operational efficiency opportunities that strengthen platform reliability and delivery quality. Requirements: - Bachelor’s degree in Information Technology, Computer Science, Statistics, Economics, Mathematics, Engineering, or another quantitative or technical discipline. - 1–3 years of experience in software development, data engineering, ETL development, data integration, analytics, or a related technical field within a complex enterprise environment. - Experience or exposure to data integration, ETL processes, data warehousing, or cloud-based data platforms. - Working knowledge of SQL and relational databases for data extraction, transformation, and analysis; Oracle and PL/SQL experience is a plus. - Basic programming experience with Python, SQL, or similar languages used in data engineering and analytics. - Working knowledge of Apache Spark, preferably PySpark, and distributed data processing concepts.
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