Senior Data Engineer

Liminal is the actionable intelligence company. We've built a proprietary Living Graph — a verified knowledge architecture that maps the real-time structure of Identity, Fraud, and Cybersecurity — and the agentic AI systems and human verification layer to make it trustworthy. Visa, Mastercard, Google, and JPMC use it to make strategic and revenue decisions. Series A, 80 people, offices in NYC, Salt Lake City, Porto, Lisbon, and Manila. The architecture works, the customers are real, and we're scaling the team.

Liminal's intelligence platform runs on the Living Graph — a proprietary knowledge architecture that maps 2.5M+ entities across Identity, Fraud, and Cybersecurity and the relationships between them. That graph is only as good as the data infrastructure underneath it. We're looking for a Data Engineer to build and maintain the pipelines, architectures, and systems that keep the graph fed, fast, and reliable.

You'll design and optimize data pipelines that ingest, process, and transform large-scale datasets for AI models, product teams, and internal operations. You'll work across cloud infrastructure, ETL development, and data quality — ensuring the systems that power verified intelligence for Visa, Mastercard, Google, and JPMC are scalable, performant, and built to compound.

This is infrastructure work that matters. Every pipeline you build, every system you optimize, directly improves the intelligence that enterprise customers use to make high-stakes decisions.

In your first 30 days, you've mapped the current data architecture, understand the pipeline landscape, and have shipped your first improvement to an existing workflow.

In your first 90 days, you've owned the design and deployment of a new pipeline end-to-end, improved reliability or performance on a critical data path, and become the go-to partner for at least one cross-functional team.

In your first year, you've meaningfully improved the scalability and resilience of Liminal's data infrastructure, built systems that other teams depend on daily, and shaped the engineering standards for how data moves through the platform.

• Design, build, and maintain data pipelines that ingest, process, and transform large-scale datasets for AI/ML models, product features, and internal analytics
• Build and optimize ETL processes that move data reliably between systems, databases, and analytics platforms — handling both batch and real-time processing
• Develop and maintain cloud-based data infrastructure (GCP, AWS, or similar), ensuring systems are robust, cost-effective, and built to scale with growing data volumes
• Implement data storage solutions and distributed databases that integrate cleanly with internal systems and product workflows
• Establish data validation, quality checks, and governance processes to ensure consistency, integrity, and compliance across all pipelines
• Set up monitoring, alerting, and automation for pipeline health, performance, and data anomalies — reducing manual intervention and catching issues before they reach production
• Collaborate with data scientists, product engineers, AI teams, and business stakeholders to ensure data is structured, accessible, and optimized for downstream use
• Maintain clear documentation of architectures, pipeline designs, and system configurations so other teams can build on your work

You've built and optimized large-scale data systems in production and know how to design pipelines that are reliable, performant, and maintainable. You're proficient in Python (including OOP and software development best practices) and SQL, with hands-on experience across cloud platforms and services for data storage, processing, and orchestration. You've worked with ETL frameworks, workflow orchestration tools like Airflow, and containerization technologies (Docker, Kubernetes, or equivalent). You understand data security, privacy, and governance — and you build with those constraints from the start, not as an afterthought. You communicate clearly across technical and non-technical teams and care about documentation as much as deployment.

Experience with GCP services (BigQuery, Cloud Run, Cloud Run Functions). Familiarity with Apache Spark or distributed processing frameworks. Understanding of REST APIs and their role in data integration. Exposure to data modeling for AI and machine learning pipelines. Experience working in a high-growth or startup environment where you've had to build systems that scale ahead of demand.

You'll build infrastructure that powers real decisions. This isn't a data warehouse no one looks at. The pipelines you build feed a Living Graph that Visa, Mastercard, and Google rely on for competitive intelligence, regulatory monitoring, and market strategy.

The engineering problems are real. 2.5M+ entities. Continuous ingestion across patents, filings, code repositories, news, hiring data, and regulatory documents. Batch and real-time processing. Neuro-symbolic AI models that depend on verified, structured data to reason — not just retrieve. You'll build the plumbing for a compound AI system, not a CRUD app.

Your work compounds. Every pipeline you build, every system you harden, makes the Living Graph smarter and the platform more valuable. You're not shipping features that stagnate — you're building infrastructure that gets better with every signal.

We respect your time. Here's what to expect:

Recruiter Screen → Hiring Manager Interview → Behavioral Interview → Practical Interview → CEO Interview → Offer