The future is what you make it.

Whenyou join Honeywell, you become a member of our global team of thinkers,innovators, dreamers and doers who make the things that make the future.

Thatmeans changing the way we fly, fueling jets in an eco-friendly way, keepingbuildings intelligent and safe and even making it possible to breathe on Mars

Workingat Honeywell isn’t just about developing cool things. That’s why all of ouremployees enjoy access to dynamic career opportunities across different fieldsand industries.

Are you ready to help us make the future?

Join a team that is elevating our strategy to drive advanced analytics and visualization tools across the Commercial enterprise. In this role, Data Engineer – Commercial, you will design, implement, and manage the data architecture, systems, and processes to effectively collect, store, process and analyze high volume, high dimensional data to provide strategic insight into complex business problems. This will involve creating and maintaining scalable, efficient, and secure data pipelines, data warehouses, and data lakes. You need to ensure consistency in data quality and availability for analysis and reporting including compliance with data governance and security standards

RESPONSIBILITIES: 

• Work in complex data science and analytics projects in support of the Commercial organization.
• Work with product owner to identify the data requirements and design/ maintain/ optimize data pipeline to ingest, transform, and load structured and unstructured data from various sources into the data warehouse or data lake.
• Design and implement data models and schemas to support analytical and reporting requirements.
• Collaborate with data scientists and analysts to define and structure data for effective analysis and reporting.
• Develop and maintain ETL (Extract, Transform, Load) processes.
• Administer, optimize, and manage databases, data warehouses, and data lakes to ensure performance, reliability, and scalability.
• Enforce data governance policies, standards, and best practices to maintain data quality, privacy, and security.
• Create and maintain comprehensive documentation for data architecture, processes, and systems.
• Troubleshoot and resolve data-related problems and optimize system performance.
• Partner with IT support team on production processes, continuous improvement, and production deployments.