Software Engineer, Data Foundations

Other

• Build and scale connectors to a wide variety of SaaS and on-prem systems (Google Workspace, Microsoft 365, Slack, Salesforce, Jira, ServiceNow, GitHub, etc.).
• Handle full syncs, low-latency incremental updates via webhooks/APIs, rate-limiting, and complex authentication flows.
• Build advanced capabilities in datasources like actions, live-fetch, and query language support.
• Transform raw, unstructured enterprise content into rich, structured, permission-aware representations optimized for search and LLM reasoning.
• Design document schemas and enrichment pipelines (entity extraction, access-graph propagation, redactions, etc.).
• Expand the capabilities of AI products through deep integrations that allow us to automate tasks, perform complex queries grounded in enterprise data, and enhance our indexed corpus with live data.
• Own end-to-end correctness, freshness, and performance for petabyte-scale data flows.
• Solve hard problems in ordering, idempotency, exactly-once processing, backpressure, and retries across distributed queues, workers, and storage.
• Preserve fine-grained ACLs, deletions, and sensitivity constraints so AI answers are always grounded in what users are actually allowed to see.
• Partner closely with Search Serving, Product, Platforms, and Security teams to define how enterprise context is exposed to LLMs and agents.
• Continuously improve observability, alerting, and automation to onboard larger customers and more data sources with confidence.
• 3+ years building production backend or data infrastructure systems (Java, Go, C++, Python, etc.).
• Hands-on experience with distributed systems, data pipelines, queues, and large-scale storage (SQL/NoSQL).
• You think in SLOs, error budgets, failure modes, and correctness guarantees — not just features.
• Comfortable with strict consistency and permission-modeling challenges.
• Prior work on enterprise connectors, search/indexing, information retrieval, or security-sensitive systems is a strong plus.
• Passionate about making AI trustworthy by building the rock-solid data foundation underneath it.
• Power user of LLMs and AI tools in your own workflow.
• This role is hybrid (4 days a week in either our San Francisco or Mountain View office)