To succeed in your interviews, you need to understand exactly how Anduril Industries evaluates its engineering candidates. The onsite loop will test your technical depth, your architectural foresight, and your ability to collaborate.
Data Modeling and Pipeline Engineering
This area evaluates your core ability to move, transform, and store data efficiently. At a hardware-and-software company like Anduril, data comes in various shapes and speeds, from structured operational metrics to high-velocity sensor streams. You need to prove you can build reliable pipelines that handle these diverse workloads without dropping critical information.
Be ready to go over:
- Batch vs. Streaming Processing – Understanding when to use Airflow and Spark versus Kafka or other real-time streaming tools.
- Data Warehousing and Data Lakes – Designing schemas (e.g., Star schema, Snowflake schema) and optimizing storage formats (Parquet, ORC) for query performance.
- ETL/ELT Best Practices – Handling data quality, idempotency, and pipeline failure recovery.
- Advanced concepts (less common) – Geospatial data indexing, time-series database optimizations, and edge-computing data synchronization.
Example questions or scenarios:
- "Design a data pipeline that ingests continuous telemetry data from a fleet of autonomous drones and makes it available for real-time dashboarding."
- "How would you handle late-arriving data in a daily batch ETL job?"
- "Walk me through how you would optimize a highly complex, slow-running SQL query used by the analytics team."
Coding and Algorithmic Problem Solving
While you are not interviewing for a generalist software engineering role, your coding skills must be sharp. Data Engineers at Anduril write production-level code to build infrastructure, automate deployments, and transform complex datasets. Interviewers want to see clean, maintainable, and efficient code, typically in Python or SQL.
Be ready to go over:
- Advanced SQL – Window functions, complex joins, CTEs, and query execution plans.
- Python for Data Engineering – Data manipulation using Pandas, interacting with APIs, and writing efficient data parsing scripts.
- Data Structures and Algorithms – Basic algorithmic complexity (Big O notation) and using the right data structures for efficient data processing.
- Advanced concepts (less common) – Concurrent programming, memory management in Python, and custom Spark UDFs.
Example questions or scenarios:
- "Write a Python script to parse a nested JSON payload from a sensor API and flatten it into a relational format."
- "Given a table of user session logs, write a SQL query to find the top 3 longest sessions for each user."
- "Implement a function to merge two large, sorted datasets efficiently without loading both entirely into memory."
Cross-Functional Collaboration and Product Sense
Because your final round includes an interview with a Product Manager, this is a distinct and crucial evaluation area. Anduril builds complex products for end-users in defense and security. Your data infrastructure must serve these products. You are evaluated on how well you understand the "why" behind the data, not just the "how."
Be ready to go over:
- Requirement Gathering – Translating vague product needs into strict data engineering requirements.
- Trade-off Communication – Explaining technical debt, latency trade-offs, or infrastructure costs to non-technical stakeholders.
- User-Centric Engineering – Understanding how data latency or inaccuracy impacts the end operator using the Lattice OS.
Example questions or scenarios:
- "Tell me about a time you had to push back on a product requirement because it was technically unfeasible or too costly."
- "How do you ensure the data pipelines you build actually solve the problem the product team is trying to address?"
- "Explain a complex data architecture concept to me as if I were a stakeholder with no technical background."
