Data Engineer

Overview

Remote Data Engineer on RTX's Corporate Enterprise Services team, building and maintaining the data foundation that powers analytics and AI capabilities across RTX.

What you'll do

• Design, implement, and maintain data governance and architecture capabilities using Databricks Unity Catalog, including namespaces, permissions, lineage, and data access controls.
• Implement automated data quality checks, monitoring, and alerts, coordinating remediation workflows with data owners and source teams.
• Integrate data platforms with Microsoft Fabric and expose trusted Gold-layer data products through enterprise data marketplaces.
• Develop and maintain reference architectures, data engineering standards, and technical documentation.
• Build data annotation workflows and labeling interfaces in Databricks to support machine learning and AI initiatives.
• Track annotation progress, data quality, and dataset versions to support reliable ML training and inference.
• Develop LLM and Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embedding generation, and vector search.
• Support machine learning pipelines through feature engineering, batch and real-time scoring, and data monitoring.
• Maintain MLflow capabilities for experiment tracking and model registry management.
• Design and maintain data classification schemas, taxonomies, and labeling specifications for signals, severity levels, categories, and other business requirements.
• Own and enhance key Gold-layer data products, including experience arrays and friction signal aggregates.
• Integrate new data sources, implement business rules, and develop consumption-ready data views and APIs.
• Maintain data dictionaries, product documentation, and metadata to support consistent data usage across the organization.
• Collaborate with cross-functional teams in an Agile development environment to manage priorities, solve complex problems, and deliver scalable data solutions.

What you'll need

• A University Degree or equivalent experience and a minimum of 8 years of prior relevant experience, or an Advanced Degree in a related field and a minimum of 5 years of experience.
• 5+ years of experience in data engineering or analytics engineering, preferably using Databricks or a similar lakehouse platform.
• Experience with SQL, Python, and Delta Lake.
• Experience with Unity Catalog, data governance, and data quality management.
• Experience building and maintaining data pipelines for machine learning training and inference.
• Experience with data modeling, classification schemas, and taxonomies.
• Extensive experience with data warehousing concepts, dimensional modeling, and ETL processes.
• Experience working in an Agile development environment and managing multiple priorities in a fast-paced environment.
• U.S. citizenship is required, as only U.S. citizens are authorized to access information under this program/contract.

Nice to have

• Experience with Microsoft Fabric, Delta Sharing, or cross-platform data sharing.
• Familiarity with annotation tools, natural language processing (NLP), and vector databases.
• Experience with MLflow or similar tools for experiment tracking and model management.
• Background in metrics and KPIs, information architecture, or IT and infrastructure domains.
• Strong analytical and problem-solving skills, with the ability to develop innovative solutions that improve business performance.
• Demonstrated leadership, interpersonal, and communication skills, including the ability to influence stakeholders and align teams toward common goals.
• Ability to manage multiple projects simultaneously while adapting to changing business and technical priorities.
• A flexible, collaborative mindset and the ability to thrive in a dynamic environment.

Details

• Remote role, working primarily offsite (from home); may require occasional onsite presence if near an RTX site, and in-person interview steps may be required.
• U.S.-based role; security clearance not required.
• Salary range 107,500 USD - 204,500 USD.