openashbyhqfelicis
Backend and Data Engineer
Condor Software
LocationSan Francisco
WorkplaceHybrid
EmploymentFullTime
Posted2026-02-03T19:26:39.134+00:00
Last observed2026-07-02 08:32:56.877436
Job idfelicis-condor-software:ashbyhq:98c029bb-878c-4af4-9b95-fbae2441161a
ABOUT CONDOR Every year, hundreds of billions of dollars are invested to discover and develop new therapies, yet the financial infrastructure behind that work has not kept pace. Clinical operations and finance live in disconnected worlds, forcing teams to make high-stakes decisions using fragmented tools and static data. Condor exists to change that. We are a system of action, building the financial intelligence layer that will power the next era of clinical development. Condor connects clinical operations, vendor activity, and financial signals into a single, real-time intelligence layer, giving R&D and finance leaders true command over how their organizations operate. Condor is pharma-native, AI-driven infrastructure built to scale industry standards we helped define with Big 4 partners. It powers prediction, control, and execution across the most complex R&D environments in the world. WHY THIS MATTERS NOW Condor has moved past proving the concept. Enterprise teams already trust Condor to run critical operations and finance. The work ahead is the hardest part: scaling something people depend on when the stakes are this high. Condor is a high-growth company backed by top institutional partners like Felicis and 645 Ventures, growing rapidly with Top 200 biopharma companies. This is a rare opportunity to help build foundational infrastructure that will shape how new therapies reach patients. THE ROLE We are looking for a Senior Backend and Data Platform Engineer to help build the core data infrastructure behind Condor’s financial intelligence platform. This role sits at the heart of how Condor turns complex clinical and financial activity into intelligence that enterprise biopharma teams trust to run their operations. You will design and own the data foundations that power Condor’s financial engine and AI-driven capabilities. That means modeling highly complex, high-stakes data, building reliable pipelines and services, and ensuring that downstream product features and intelligence workflows operate with accuracy, consistency, and scale. The systems you build will directly support mission-critical finance and operational use cases, not dashboards or experiments. This is a hands-on, senior engineering role with real ownership. You will work across backend services, data pipelines, and APIs, taking features from design through production. You will help define schemas, transformations, and architectural patterns that become the backbone of the platform as it scales. While the primary focus is backend and data engineering, you are expected to engage pragmatically across the stack to ensure data and intelligence are surfaced correctly in the product. This role is for engineers who want to build durable infrastructure under real-world complexity, where correctness, trust, and scale are not optional, and where the systems you design will shape how an entire industry operates. KEY RESPONSIBILITIES - Design, build, and maintain scalable data pipelines that ingest, normalize, and transform financial and clinical trial data from multiple internal and external sources, with a focus on making data suitable for analytics, reporting, and LLM-based AI agents. - Develop backend services and data access layers that expose high-quality financial data to internal systems and customer-facing features, ensuring data is structured for direct consumption by LLMs and automated workflows. - Implement and operate embedding pipelines and vectorized representations of structured and semi-structured data to support semantic search, RAG, and agentic workflows. - Optimize database performance, query execution, and batch processing jobs to support large-scale financial datasets and AI-driven access patterns. - Participate in architectural decisions around data infrastructure, storage strategies, and integration with LLMs, embeddings, and vector databases. - Work closely with product managers and analysts to translate business requirements into durable datasets
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