Data Engineer (JOB ID 002697)

Our client is looking to bring on Data Engineers to design, develop, and implement an integrated data system supporting Aerospace and aftermarket program material requirements. The goal is to enable advanced Event-Based Planning (EBP) and Materials 360 initiatives- optimizing material forecasting, maintenance planning, and inventory management for Aerospace aftermarket operations. These resources will work on high-impact team, collaborating with AI/ML specialists and data engineering peers to deliver scalable, cloud-native solutions that improve visibility and alignment between demand signals and material planning. REQUIREMENTS: • 7+ years of hands-on Data Engineering experience building data pipelines in an enterprise environment • 5+ years of ETL development background - building, maintaining, and optimizing data pipelines • Expertise in Azure Cloud Services and Databricks (or similar cloud-based data environments) • Proficiency in Python and PySpark for data transformation and automation • Strong SQL skills with experience across multiple databases (Postgres, SQL Server, etc.) • Experience with structured and unstructured data ingestion and transformation • Understanding of data governance, data quality, and data architecture principles • Excellent communication skills and ability to interface with technical and non-technical stakeholders PLUS SKILLS: • Experience within aerospace, manufacturing, or defense environments • Experience supporting forecasting, MRP, or AI/ML data pipelines • Knowledge of data visualization tools (Power BI, Qlik, etc.) and alerting/reporting frameworks • Experience with API development, CI/CD, or DevOps pipelines in Azure • Background in data governance RESPONSIBILITIES: • Architect and build an integrated Event-Based Planning (EBP) data system capable of supporting both MRO material planning (short-term and long-term) and forecast maintenance activities. • Develop a pBOM (Planning Bill of Materials) management application to maintain workscope, exposure, and replacement rate data as input for forecasting • Design and develop scalable data pipelines and integrations in Azure/Databricks • Build automated ETL workflows supporting forecasting, analytics, and AI/ML applications • Ingest and transform data from multiple systems (e.g. SAP, repair logs, material databases) • Ensure data quality, consistency, and governance across structured and unstructured datasets • Create dashboards, reports, and alerts to support real-time material and maintenance decision-making • Partner with cross-functional teams to gather business requirements and translated them into data solutions • Support deployment, orchestration, and automation for data pipelines and assets • Contribute to the long-term data architecture vision for aftermarket forecasting and inventory optimization