À propos
We started Span to address a pattern wed all lived: as teams expand, context is buried under layers of meetings, tools, people and process. Things begin to stall in the outer loops like prioritization, allocation, collaboration, and managementareas where mistakes and inefficiencies are arguably more costly. We are creating a new type of tool that delivers coherent and useful software engineering context for humans and agents alike, a tool that we believe will enable ownership, autonomy, and superhuman impact inside an engineering organization. Were building a future where: Engineering leaders can understand their most precious resource team time so that theyre able to invest intentionally, root out hidden costs of toil, and unlock the true potential of their team. Everyone inside the organization can have an intuitive grasp of whats happening so theyre empowered to make better decisions, operate confidently and deliver predictably. The hours tied up in tagging, status reporting and coordination meetings are automated away so that people spend less time on busywork and more time on the real work.
In this role youll:
Develop a connector for a new integration, such as BitBucket or Shortcut. Design and implement a unified data model for a new category of data relevant to the Software Engineering Intelligence space, such as incident management systems or monitoring systems. Convert data pipelines to incremental workloads to improve performance, out event based data processing framework that scales. Create an AI Agent workflow backend that provides tools for LLMs to query a knowledge graph to produce reports and insights for our customers.
What were looking for:
Youve built products with a backend or data lean, ensuring the app is prepared to scale, solving all kinds of bottlenecks and enabling Full-Stack and Front-End engineers to create delightful UI experiences. Experience working with Data pipelines and ETL processes, data modeling and optimization techniques. Experience spinning up your own projects in a cloud environment before, ensuring uptime and budget constraints are met. Experience integrating Large Language Models (LLMs) and understanding of prompt engineering in personal or professional projects. Passion for technology, innovation, and learning and you can prove it.
About our Stack:
TypeScript, NestJS, React, Postgres, Redis Airbyte, Dagster, Python, Kafka AWS, K8S, FluxCD
Backed by dozens of founders and CTOs we admire from places like Slack, Notion, Rippling, Fivetran and Coda, CPOs from places like Adobe and Square, as well as funds like Alt, BoxGroup, Bling, Craft, and SV Angel who dare to dream with us. Compensation Range: $100K $200K #J-18808-Ljbffr
Compétences linguistiques
- English
Avis aux utilisateurs
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