Data Engineer

11x.ai•San Francisco (On-Site),

114d•$170,000 - $200,000•Hybrid

About The Position

We're looking for a Data Engineer who wants to operate like a founder. Not someone who wants to spend their time maintaining dashboards, moving tickets across a board, or optimizing pipelines in a mature environment. Someone who sees infrastructure as a product, thrives in ambiguity, and wants to build the systems that power the next generation of AI applications. This is not a traditional data engineering role. You'll own critical pieces of the infrastructure that power our AI workers, customer data platform, and future data architecture. You'll work across data engineering, backend systems, infrastructure, and AI, building foundational systems that directly impact how our products behave in the real world. The best people for this role think in systems, move with urgency, and use AI as leverage. The role of data engineering is changing. As AI becomes more capable, the bottleneck increasingly shifts from models to data. The best AI systems aren't defined by the model they're using. They're defined by what they know, what they can access, and how effectively they can retrieve and reason over information. At 11x, we're building AI workers that operate on behalf of our customers. For those workers to become more useful, more autonomous, and more intelligent, they need access to the right data at the right time. Every improvement in data access, retrieval, enrichment, synchronization, and infrastructure directly improves the capabilities of our agents. That means the systems you build won't sit behind the scenes. They'll determine what our AI workers can understand, what actions they can take, and how much value they can deliver. The quality of our AI workers is ultimately constrained by the quality of the data systems behind them. You'll be responsible for pushing that frontier forward. We believe the future belongs to engineers who: think like owners, build systems that scale beyond themselves, use AI tools fluently, care about outcomes, not just implementation, move quickly through ambiguity, enjoy building foundational infrastructure from first principles. If that excites you, you'll probably love working here.

Requirements

4+ years of software engineering or data engineering experience
Strong experience building and maintaining production-grade data systems and pipelines
Experience with Python and Typescript
Strong backend engineering fundamentals beyond traditional data engineering
High agency — you naturally move things forward without waiting for direction
Comfort operating in ambiguity and building without a playbook
Strong systems thinking and architectural intuition
Ability to balance speed with long-term scalability
Strong problem-solving skills and a willingness to own problems end-to-end
Hungry to grow. You want expanding scope, responsibility, and ownership over time
Excitement about AI-native workflows and the future of software development

Nice To Haves

Experience with ClickHouse or other columnar databases
Experience building customer data platforms (CDPs)
Experience with Airbyte or similar data integration and ingestion platforms
Experience with CRM integrations and synchronization systems
Experience designing data lake architecture
Experience supporting AI or ML products
You've used tools like Claude Code, Codex, Cursor, or similar heavily in your workflow
You care deeply about engineering velocity and iteration speed

Responsibilities

Own and extend our customer data ingestion platform
Build and maintain large-scale data pipelines powering AI products and customer workflows
Design systems for syncing customer data across external platforms, CRMs, and third-party systems
Help architect our future data lake, retrieval layer, and data infrastructure strategy
Build ingestion and querying systems for lead, account, enrichment, and customer knowledge data
Create the infrastructure that gives our AI workers access to the information they need to reason, act, and improve over time
Partner closely with product and engineering teams to unlock new AI product capabilities
Improve reliability, observability, performance, and scalability across our data stack
Contribute across backend systems and infrastructure, not just traditional data engineering projects
Push ideas into production quickly instead of over-optimizing in planning phases