Data Engineer

Design, develop, and maintain cloud-native data pipelines using Databricks, Microsoft Azure Data Factory, and Microsoft Fabric to support data integration and analytics solutions. Implement incremental and real-time data ingestion strategies using a medallion architecture for data lake storage. Write, optimize, and maintain complex SQL queries to transform, integrate, and analyze data across enterprise systems. Develop solutions with a focus on scalability, maintainability, testability, and long-term operability within a continuous delivery mindset. Data Operations & Reliability Support and troubleshoot legacy data platforms built on SSIS and SQL Server, ensuring high availability and performance of critical data processes. Identify, troubleshoot, and resolve data integration and data quality issues to ensure reliable production data delivery. Contribute to observability, automated validation, and CI/CD pipelines to support fast feedback and safe releases. Agile Collaboration & Engineering Practices Collaborate daily with product owners, QA, and engineers through Scrum ceremonies including standups, backlog refinement, sprint planning, sprint reviews, and retrospectives. Participate in proof-of-concept efforts, technical spikes, and design discussions, providing thoughtful technical analysis and pragmatic recommendations. Apply XP engineering practices such as pairing, incremental delivery, continuous refactoring, and shared code ownership to maintain high-quality, evolvable systems. Clearly communicate technical concepts, risks, tradeoffs, and progress to both technical and non-technical stakeholders. Requirements Required Qualifications 5+ years of experience designing and building data solutions. Strong proficiency in SQL and Python for data analytics and transformation. Hands-on experience with ETL pipeline development and automation. Solid understanding of data lake architecture and design principles. Experience working on Agile teams (Scrum, XP, or similar), with regular participation in standups, sprint planning, refinement, reviews, and retrospectives. Comfort operating in highly collaborative environments with frequent verbal and written communication across engineering, product, and QA teams. Ability to break work into small, iterative deliverables and adapt quickly based on feedback and changing priorities. Strong ownership mindset, including accountability for the quality, reliability, and maintainability of delivered solutions. Preferred Qualifications Experience with Azure cloud services and cloud-based ETL tools. Familiarity with data visualization tools such as Power BI or Tableau. Understanding of event-driven architectures, including queues, batch processing, and pub/sub models. Exposure to NoSQL databases such as MongoDB or Cassandra. Experience working on product engineering teams delivering customer-facing or operationally critical systems. Familiarity with modern engineering practices inspired by XP, including automated testing, pairing, refactoring, and continuous integration. Experience operating systems in production environments with uptime, reliability, and observability expectations. Bonus Points For Experience in Data Science or Machine Learning, particularly in model deployment or feature engineering. Experience contributing to engineering standards, documentation, or continuous improvement initiatives within an Agile team. #J-18808-Ljbffr Stellar IT Solutions