Data Application Engineer

At Parallel Bio, we are leveraging the human immune system to unlock safer, more effective drugs. We believe immunotherapies are the future of medicine, but their discovery is hindered by outdated models that fail to capture the complexity of the human immune system.

Our platform overcomes these challenges by combining best-in-class human immune organoids with massive scale and advanced computational methods, including AI and machine learning. This allows us to generate unprecedented, population-scale insights into human health and disease.

We can rapidly discover new drugs that we know will work in patients from the start and understand how they will perform across an entire population—something not possible with today's technology. This knowledge will allow us to engineer therapies that will work for as many people as possible, ensuring a safe and effective cure for everyone.

We are a fast-paced, venture-backed company at a pivotal moment of growth. Join us on our journey as we create new tools to push the boundaries of what is possible.

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 ou