About the job
Join our mission at Helpshift, where we aim to revolutionize customer service by providing a seamless, mobile-first experience. In a world where consumers expect immediate and respectful interactions, our platform empowers B2B brands to engage with their customers through innovative in-app chat solutions. We have transformed traditional customer service methods, moving away from sluggish email exchanges to dynamic real-time conversations that prioritize user convenience.
Our cutting-edge AI-powered chatbots and automation tools enable brands to offer instant resolutions, enhancing the role of customer service agents by equipping them with powerful automation tools that work seamlessly. Trusted by industry leaders such as Scopely, Supercell, Brex, EA, and Square, Helpshift is currently enabling over 900 million active consumers on more than 2 billion devices globally.
Our Impact:
- 85,000 requests per second
- 30 milliseconds average response time
- 300 GB data transferred per hour
- 1,000 virtual machines deployed during peak times
About Our Team:
At Helpshift, we understand that consumers value their time. Therefore, we provide brands with essential insights into their customer service operations through various data products, including in-app analytics dashboards and data-sharing integrations.
The Data Platform team is pivotal in creating and maintaining the infrastructure required to support these analytics products at scale. We are responsible for building and managing robust data pipelines, databases, and other data structures to ensure data reliability and accessibility. Our team also collaborates with internal business intelligence and machine learning groups to provide data operations support, handling 2 million events per minute and processing over 1 terabyte of data daily.
Your Role:
- Design and maintain robust data pipelines for both data ingestion and operational analytics, utilizing data from 2 billion devices and 900 million monthly active users.
- Develop customer-facing analytics products that provide actionable insights and facilitate anomaly detection.
- Work closely with data stakeholders to address their data needs and participate in the analytical process.
- Draft design specifications, test plans, deployment strategies, and scaling methodologies for data pipelines.
- Mentor team members and contribute to organizational knowledge sharing.