Data Engineer (JOB ID 002767)

Our client is seeking a Data Engineer to design, build, and maintain scalable data pipelines and cloud-based data infrastructure that enable advanced analytics, AI, and data-driven decision-making across the organization. This role will support a manufacturing environment leveraging SAP, Databricks, and Microsoft Azure, integrating data from enterprise systems, operational platforms, and external sources into a modern lakehouse architecture. REQUIREMENTS • 5+ years of being a Data Engineer creating data pipelines using Databricks • Strong proficiency in SQL and Python • Hands-on experience with data analytics platforms including Databricks, and building reliable ETL/ELT pipelines using orchestration tools • Working proficiency of Azure cloud platforms, including cloud-native data services • Experience integrating and working with SAP data platforms, including SAP BW, SAP Datasphere, and SAP Business Data Cloud • Background supporting advanced analytics or machine learning pipelines, with exposure to CI/CD, Git-based version control, infrastructure-as-code, and LLMs / prompt engineering preferred • Ability to come onsite 2-4 days a month in Lancaster, PA PREFERRED SKILLS & CERTS • Technical certifications in SAP, Databricks, Azure, or AI/ML • SAP Datasphere experience RESPONSIBILITIES • Design, build, and maintain scalable batch and real-time data pipelines to support analytics, reporting, and AI initiatives • Develop and manage a cloud-based data lakehouse using Databricks on Microsoft Azure, ensuring performance, scalability, and cost efficiency • Ingest, integrate, and harmonize data from SAP systems (SAP BW, Datasphere, Business Data Cloud) and other enterprise and external data sources • Design and implement efficient data models to enable reliable analytics, machine learning, and business intelligence • Develop robust ETL/ELT workflows using modern tools (e.g., Databricks, dbt, Airflow) and automation best practices • Implement data quality, governance, monitoring, and lineage standards to ensure accuracy, reliability, and compliance • Collaborate with data scientists, analysts, and business stakeholders to deliver trusted, analytics-ready datasets and continuously improve data solutions