LIFE AT PLUME

At Plume, we believe that technology isn’t about moving faster, it’s about making life’s moments better. Which is why we’ve built the world’s first, and only, open and hardware-independent service delivery platform for smart homes, small businesses, enterprises, and beyond. Our SaaS platform uses WiFi, advanced AI, and machine learning to create the future of connected spaces—and human experiences—at massive scale.
We now deliver services to over 50 million locations globally and have managed over 2.5 billion devices on our platform. We’re expanding rapidly, pioneering a new category, and we achieved our Series F funding in just four years. Our customers include many of the world’s largest Communications Service Providers (CSPs) who look to Plume to help them evolve their smart home offerings while gleaning insights from their own data.
With a bias for action and a love for being trailblazers, the team at Plume embodies a combination of relentless curiosity and imaginative innovation. We challenge ourselves to think in ways that other companies don’t, work to do what should be done (rather than what can), and if we can’t do it exceptionally well, we don’t do it. It’s how we’ve assembled a team of world-class builders, thinkers, and doers. And it’s how we’re reinventing what’s possible every day.
As a Data Engineer on Plume’s Data Products team, you will play an integral role in shaping the data platform that powers the gamut of Plume’s next generation of products and analytics. You will work with a modern data tech stack and collaborate with cross-platform teams to solve challenging problems on a daily basis.

• Architect and productionize Spark data pipelines supporting data-oriented products, machine learning, and analytics.
• Work with cross-functional teams to design end-to-end system architectures and data flows.
• Adhere to data protection requirements including data access, retention, residency, and de-identification
• Maintain up-to-date documentation of data warehouse schemas
• Systematically enhance data quality, availability, and observability
• Analyze and solve performance, cost, and scale challenges
• Refactor code as needed to improve performance and simplify operations
• Provide production support in triaging and fixing issues relating to data quality and availability
• Mentor junior team members and new hires