Floor care and pet cleaning products with direct-to-consumer and retail distribution
BISSELL is a 150-year-old, privately held floor care manufacturer scaling cloud data infrastructure (AWS, Informatica, Domo adoption) to address supply chain and demand planning friction. The hiring mix—weighted toward engineering and manufacturing roles with accelerating velocity—reflects active product development and operational modernization efforts. Pain-point clustering around forecast accuracy, supplier standardization, and new product cycle time suggests they're building out analytics and planning capabilities to compress go-to-market timelines.
BISSELL manufactures and distributes floor care, carpet cleaning, and pet hair removal products for North American consumers through direct-to-consumer and retail channels. The company operates as a privately held, family business with headquarters in Grand Rapids, Michigan, and employs between 1,001 and 5,000 people across the United States, China, Netherlands, Canada, and Singapore. The product portfolio spans vacuums, deep cleaners, proprietary cleaning solutions, and pet-specific tools. Operations include manufacturing, third-party logistics partnerships, Amazon seller operations, and internal product development and design teams.
BISSELL uses Informatica for data integration, AWS (Lambda, Glue, Redshift, SageMaker, Bedrock) for cloud analytics and ML, Oracle for enterprise systems, PTC tools (Creo, WindChill) for design and PLM, Domo for BI (currently adopting), and Adobe Creative Cloud for marketing content.
Active projects include new floor care product development, scalable ETL/ELT pipeline development, demand planning process enhancement, Amazon U.S. sales strategy, and product detail page optimization. Supply chain optimization and new product introduction cycle time reduction are priority focus areas.
BISSELL Homecare, Inc.'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.