Data Application Engineer

About the role

This role is a strategic and operational extension of leadership within the Data & Infrastructure team. You can carry full context across the department's workstreams and act as a trusted proxy, making decisions, unblocking teams, and driving execution with minimal oversight. The right candidate has fluency in modern data warehousing platforms (such as Palantir Foundry, Snowflake, Databricks, etc.), genuine curiosity about the science, and enough operational instinct to self-organize around the highest-value work without waiting to be told what to do. This is not a pure individual contributor role. It sits at the intersection of technical execution, project management, and strategic planning, and is designed for someone who can operate across those modes fluidly depending on what the department needs. What You Would Own Data Systems & Platform Infrastructure Drive the buildout of experimental data pipelines, storage architecture, and analytical tooling Define and enforce data standards, schemas, and governance as dataset volume and complexity grow; partner with Automation and Science teams to ensure those standards reflect real experimental workflows, preventing data debt before it starts Build and evolve the ontology (actions, objects, links) that represents our biological workflows in Foundry, and develop bespoke React applications that scientists and customers want to use Develop a working understanding of what data we have, what it is worth, and where the gaps are, then build workflows that unlock that value Champion data-driven discovery across the company, raising quantitative literacy and helping scientists move from raw data to insight with increasing autonomy Identify technical debt and infrastructure gaps; scope and prioritize remediation Science Team Partnership Develop a genuine, working-level understanding of science teams' priorities, experimental roadmaps, and active book of work by being in the room, not relying on secondhand summaries Ensure Data & Infrastructure builds toward what science actually needs, not what looks logical from a systems perspective in isolation Identify where data capture or pipeline gaps are creating friction for researchers and treat those with the same urgency as internal engineering priorities Build enough trust with science leadership to anticipate needs and scope work proactively Automation Team Coordination Stay current on the automation team's roadmap so that data infrastructure remains compatible with the physical platform as it evolves Where the two intersect (instrument integration, data ingestion, metadata standards), manage sequencing and dependencies sensibly without gatekeeping Ensure the data layer keeps pace with expanding automation capabilities so increased experimental volume produces well-structured datasets, not cleanup backlogs Keeping Things Moving Self-organize around the department's highest-value work; seek out, sequence, and prioritize what needs doing rather than waiting for a task list Own the operating rhythm: sprint planning, roadmap reviews, cross-functional syncs, dependency tracking Surface risks and tradeoffs early on infrastructure delivery timelines Translate technical constraints into business terms for BD, finance, and partnership discussions, where data infrastructure or security posture is relevant

Requirements

• Mandatory Experience
• Experience working with major data warehousing solutions (such as Palantir Foundry, Databricks, Snowflake, etc.) and strong fundamentals in database design
• Proficiency with React frameworks for user-facing tools and visualizations Familiarity with cloud infrastructure (AWS) and modern data engineering practices
• Startup or scale-up experience where scope is fluid and resourcefulness matters
• Exposure to life sciences data (assay data, LIMS, genomics, or similar) is desirable; you should be comfortable following a science team discussion and tran

Additional Information

The Mission Drug discovery has a translation problem: more than 95% of drugs that succeed in animal models fail in humans. We're building the alternative: human-first drug discovery, powered by organoids and AI, running on real human biology from the very first experiment. Our platform is 87% concordant with clinical patient data - a vast improvement over the 3% translational success rate of animals. We've demonstrated the ability to model immunotoxicology, immunogen stimulation, and two autoimmune diseases with more on the way. Numerous pharma partners including 3 Fortune 500 companies are already using the platform. We've raised ~$30M from AIX Ventures, Marc Benioff, Jeff Dean, and Y Combinator. With the FDA Modernization Act 3.0 and the FDA's March 2026 validation framework, the regulatory tailwinds only continue to get stronger. The opportunity ahead of the company is generational: build the first scaled engine for generating real human biological data, and use it to fundamentally change how medicines are discovered.

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Company Intel

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