ABOUT THE TEAM

The Analytics team models Anduril’s internal data into reusable entities that describe how the company actually operates, in effect a digital twin of our operations. Those entities power the workflows, metrics and applications that help Anduril make better decisions faster. We believe the most valuable thing a unified data model can do is serve as the foundation for products people rely on every day.

ABOUT THE JOB

Most data work stops at the dashboard. For this role, that’s where the work begins.

As a Product Engineer on the Data team, you’ll build internal products on top of Anduril’s unified data model. These are tools that people across Finance, Supply Chain, Manufacturing, People and Sustainment use to run their part of the business, not just monitor it. You’ll own products end to end: talking to users, deciding what to build, designing the data underneath, shipping the interface on top and measuring whether it worked.

You’re an engineer who cares more about the problem than the tech. You know data modeling well enough to extend the foundation, and you’re comfortable across the stack when a product needs it. What drives you is taking a messy operational problem, figuring out what should exist and getting it into users’ hands quickly.

We care less about the stack you already know than about how you handle problems you haven’t solved before. Maybe you’ve built React frontends, or worked with optimization algorithms. If you used them to solve real problems for real users, great. We’re looking for people who see a hard problem and think, “I want to solve that, and I’ll learn whatever it takes.”

WHAT YOU'LL DO

• Own products from 0 to 1. Find a high-leverage problem, scope it, prototype it, get it in front of users and iterate until it’s something people depend on.
• Treat operators as your customers. Spend time with the people doing the work, learn their domain in depth, and turn what they actually need (as opposed to what they first ask for) into software.
• Build across the stack. Work across the data model, backend logic and user interface. Use whatever the problem needs: a frontend, an optimization model, a forecasting engine, an LLM-powered workflow or something we haven’t thought of yet.
• Build on the ontology and extend it. Use Anduril’s unified data model as the base for your products, and push changes back into the model when a product reveals a gap.
• Ship fast and measure impact. Favor small releases and tight feedback loops. Success means the business runs differently, not just that the feature went live.
• Make product calls. Decide what’s worth building, what “good enough” looks like, what to cut and when to kill an idea.
• Raise the bar for the team. Show the rest of Analytics what’s possible when data models become products.

OUR STACK

You don’t need to know all of this on day one. You’ll pick it up quickly, and you’ll probably add to it.

• Databricks: our central foundation for data applications, visualization and large-scale analysis
• dbt: SQL transformations with version control, testing and CI/CD
• Dagster: asset-based, event-driven orchestration

REQUIRED QUALIFICATIONS

• 4+ years building software or data products as a Product Engineer, Software Engineer, Data/Analytics Engineer, or technical consultant with a strong engineering background
• A track record of shipping products from 0 to 1 that people actually used, whether at work, at a startup or on your own
• Strong product sense: you can tell a real problem from a stated one, prioritize what to build first, and make trade-offs between scope, speed and quality
• Strong data modeling fundamentals: you can design entities and relationships that generalize, and explain the trade-offs
• Solid programming skills in Python and SQL, and comfort picking up new languages and frameworks when a product calls for it
• Comfort working directly with users and non-technical stakeholders, and communicating clearly with them
• Experience working with sensitive data and following security protocols
• Eligible to obtain and maintain an active U.S. Secret security clearance

PREFERRED QUALIFICATIONS

• Frontend development (React, TypeScript) or experience building internal tools end to end
• Optimization, operations research or simulation (linear/integer programming, scheduling, routing, inventory)
• Applied ML or LLM-powered application development
• Experience with Palantir Foundry (Workshop, Ontology, OSDK) or similar operational data platforms
• Domain knowledge in manufacturing, supply chain, finance or defense operations
• Early-stage startup experience or a history of wearing multiple hats (engineering, product, design)
• A portfolio, GitHub or side project that shows you learned something hard because you wanted to