Software Engineer, Data Foundations

Glean•San Francisco, CA

15d•$140,000 - $265,000•Hybrid

About The Position

We are looking for a Software Engineer to join Glean’s Data Foundations team — the group that owns the end-to-end data ingestion and management layer powering Glean’s Search, AI Assistant, and Agent products across thousands of enterprise apps and billions of documents. Your work will directly determine the quality, freshness, and trustworthiness of the knowledge that every Glean user interacts with every day. You will work on: Ingestion & Connectivity 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. Data Processing & Modeling 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. Reliability & Distributed Systems 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. Security & Permissions Preserve fine-grained ACLs, deletions, and sensitivity constraints so AI answers are always grounded in what users are actually allowed to see. Cross-Functional Impact 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

Ingestion & Connectivity 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.
Data Processing & Modeling 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.
Reliability & Distributed Systems 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.
Security & Permissions Preserve fine-grained ACLs, deletions, and sensitivity constraints so AI answers are always grounded in what users are actually allowed to see.
Cross-Functional Impact 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.