As a Lead Analytics Engineer for Commercial Excellence here at Honeywell Aerospace Technologies, you will play a critical role in driving data-driven decision-making and providing valuable insights to support business growth and operational excellence. You will be responsible for managing analytics projects, collaborating with cross-functional teams, and delivering actionable insights to drive business outcomes and support the expansion of analytics capabilities serving hundreds of commercial users including sellers, analytics and business leaders.

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Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.

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As a Lead Data Engineer at Honeywell, you will be a pivotal role responsible for leading the design, development, and implementation of data engineering solutions within the organization, ensuring effective collection, processing, and analysis of data to support business objectives.

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“Must be a US Citizen due to contractual requirements”

YOU MUST HAVE

• At least 7 years of experience in data engineering, analytics engineering or advanced analytics
• Deep expertise in data modeling, ETL/ELT development, and analytics data architecture
• Experience with cloud data platforms such as Databricks, Snowflake or Microsoft Fabric
• Proficiency in programming languages such as Python, R, or SQL
• Experience working with machine learning datasets and supporting ML deployment pipelines
• Strong analytical, communication, and stakeholder engagement skills
  
  
  

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Analytics Engineering Leadership

• Lead the design and development of scalable analytics models, pipelines, and semantic layers that power commercial analytics and AI solutions
• Establish and promote best practices for analytics engineering, including data modeling standards, pipeline development, testing frameworks, and documentation
• Provide technical leadership and mentorship to analytics engineers and analysts while guiding the adoption of modern data engineering practices
• Architect and maintain robust data pipelines that transform raw operational data into trusted, structured datasets for analytics and decision making
• Design and build reusable commercial data products supporting the commercial function including pricing analytics, sales performance insights, forecasting and market intelligence
• Develop scalable analytics datasets and semantic models that enable self service reporting and advanced analytics across commercial teams

Data Architecture and Data Products

AI & Advanced Analytics Enablement

• Partner with Data Scientists and Decision Scientists to deploy machine learning models and AI solutions to support commercial workflows
• Develop feature pipelines and curated datasets optimized for machine learning training, inference, and predictive analytics applications
• Support the development of AI powered decision support tools and commercial copilots used by the commercial teams across pricing, sales, marketing, offering management and strategy teams

Commercial Analytics Lab Support

• Contribute to the development and scaling of the Commercial Analytics Lab, enabling rapid experimentation and deployment of analytics and AI solutions
• Collaborate with commercial subject matter experts, data scientists, decision scientist and analysts to translate business opportunities into scalable analytics solutions and data products
• Build reusable data frameworks that accelerate experimentation and model deployment
• Partner with enterprise IT and data platform teams to operationalize select successful lab prototypes into enterprise grade solutions.

Additional

• Hybrid Work Schedule Note: For the first 90 days, New Hires must be prepared to work 100% onsite M-F
• Travel: up to 10%
• Reports to: Director of Analytics & Insights