Principal Software Professional

Founding Software Architect

The next generation of AI analytics is being built to assist product, marketing and sales teams in making sense of customer feedback at scale.

This role requires experience working on a single product over a long period within a data platform organization.

Making sense of qualitative customer feedback from multiple sources is manual and time-consuming for teams to get an in-depth view of customer pain points and identify opportunities.

The transition to an AI-first world will be accelerated by enabling companies to create more value for their customers through a deeper understanding of needs.

Teams are equipped with super powers to go beyond tracking explicit user actions to gleaning insights from unstructured customer feedback data from multiple sources and touchpoints.

This is done by simplifying complex data collection and reconciliation, automating AI insight extraction at scale and building agents that handle insights on behalf of teams without needing data scientists or analysts.

Over 500M user signals have been processed to date and several data-driven Enterprise companies are served.

If you're excited about the potential to redefine what the future of data-driven organizations should look like, then you might have found your place here.

Key Responsibilities

• Design and own scalable backend services for our data-intensive web app and AI / ML model serving.
• Make database and storage trade-offs to optimize for performance when managing large amounts and varieties of data.
• Build robust batch and real-time data pipelines that can scale with increased load and data.
• Build and maintain integrations with different data sources and platforms (source and sink).
• Design and manage the evolution of flexible data models and database infrastructure to handle different types of data sources and data points.
• Implement and manage workflow orchestration for multiple data jobs.
• Setup robust testing practices, monitoring and observability to ensure reliability of all services and data pipelines.

Stay up-to-date with software engineering and data engineering best practices.

Qualifications

A bachelor's or master's degree in computer science or a related field is required.

Experience building scalable data pipelines, orchestrating workflows and optimizing pipelines is necessary.

Experience working with data warehouse technologies and strongly typed languages such as TypeScript / NodeJS / Python is required.

Ability to work across the data and backend is essential.

Comfortable managing cloud infrastructure for backend, ML and data pipelines at scale (DevOps).

Ability to work collaboratively with cross-functional teams including frontend and machine learning engineers.

Familiarity with vector databases / stores.

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