What is a Solutions Architect at Association Of Universities For Research In Astronomy?

As a Solutions Architect (specifically operating as a Data Systems Architect) at the Association Of Universities For Research In Astronomy (AURA), you are stepping into a role that directly enables humanity’s exploration of the universe. AURA operates world-class astronomical observatories—including the National Solar Observatory (NSO), NOIRLab, and the Space Telescope Science Institute—on behalf of the National Science Foundation and NASA. In this role, you are the technical linchpin responsible for designing the systems that capture, process, archive, and distribute petabytes of astronomical data to scientists worldwide.

The impact of this position cannot be overstated. You will be dealing with massive scale and extreme complexity. Telescopes generate unprecedented volumes of data every night, requiring robust, fault-tolerant, and high-performance data pipelines. Your architectural decisions will directly dictate how quickly and reliably researchers can access critical observations, ultimately accelerating scientific discovery.

Expect to work at the intersection of advanced software engineering, high-performance computing (HPC), and scientific research. You will collaborate closely with astronomers, data scientists, and software engineers to bridge the gap between complex scientific requirements and scalable technical realities. This is not a standard enterprise IT role; it is a mission-driven opportunity to build the infrastructure that helps answer some of the most profound questions in astrophysics.

Getting Ready for Your Interviews

Preparing for an interview at AURA requires a strategic blend of deep technical review and an understanding of the scientific research environment. You should approach your preparation by focusing on how you build scalable systems that serve highly specialized, data-heavy user bases.

Your interviewers will evaluate you against several key criteria:

Technical and Domain Expertise – This evaluates your mastery of system design, data architecture, and infrastructure. Interviewers will look for your ability to design hybrid cloud and on-premise solutions, manage large-scale data lakes, and optimize high-throughput data pipelines. You can demonstrate strength here by drawing on past experiences where you successfully scaled data systems or migrated legacy architectures to modern frameworks.

Problem-Solving at Scale – This criterion focuses on how you approach complex, ambiguous architectural challenges. At AURA, data latency, storage costs, and network bandwidth are constant constraints. You will be evaluated on your ability to break down these constraints, evaluate trade-offs, and propose pragmatic, scalable solutions.

Cross-Functional Leadership – As an architect, your ability to influence without direct authority is critical. Interviewers want to see how you communicate complex technical concepts to non-technical stakeholders, specifically research scientists and academic partners. Strong candidates will showcase active listening, patience, and the ability to translate scientific goals into engineering roadmaps.

Mission and Culture Fit – AURA is a highly collaborative, mission-driven organization. Interviewers will assess your adaptability, your comfort with the rigorous pace of academic-adjacent research, and your genuine interest in the astronomical sciences. Demonstrating a passion for open science and long-term data preservation will set you apart.

Interview Process Overview

The interview process for a Data Systems Architect at AURA is rigorous, thorough, and heavily focused on both technical depth and collaborative fit. You can expect a process that balances standard software architecture evaluations with deep dives into how you handle massive datasets and unique scientific use cases. The pace is typically deliberate, reflecting the organization's academic and research-oriented culture.

You will generally start with a recruiter phone screen to align on your background, expectations, and the specifics of the Boulder-based role. This is followed by a technical screening with a hiring manager or lead engineer, focusing on your past architectural decisions and high-level system design capabilities. The core of the evaluation takes place during the onsite (or virtual onsite) panel, which frequently includes a formal presentation. You will be asked to present a past architecture project to a mixed audience of engineers and scientists, followed by deep-dive interviews covering system design, behavioral leadership, and technical problem-solving.

What makes this process distinctive is the emphasis on peer review and consensus. AURA values candidates who can defend their technical choices gracefully while remaining open to feedback from domain experts.

The visual timeline above outlines the standard progression from initial screening to the final panel rounds. Use this to pace your preparation, ensuring you have a polished architectural presentation ready for the onsite stage, while also preparing for the distinct technical and behavioral interviews that follow.

Deep Dive into Evaluation Areas

System Architecture and Data Pipelines

At the core of the Data Systems Architect role is the ability to handle massive, continuous streams of data. Telescopes and sensors generate terabytes of data per night, which must be ingested, processed, and archived without data loss. Interviewers will evaluate your ability to design resilient pipelines that can handle high throughput, manage backpressure, and ensure data integrity. Strong performance means you can comfortably discuss decoupling components, utilizing message brokers, and designing for fault tolerance.

Be ready to go over:

• Ingestion Strategies – Handling high-velocity data streams and batch processing.
• Storage Tiering – Designing hot, warm, and cold storage architectures to manage petabytes of data cost-effectively.
• Workflow Orchestration – Using tools like Airflow or Argo to manage complex data processing dependencies.
• Advanced concepts (less common) – Optimizing network protocols for massive file transfers (e.g., FITS files) across geographically distributed observatories.

Example questions or scenarios:

• "Walk me through how you would design a data ingestion pipeline for a remote sensor that generates 10TB of data per night with intermittent network connectivity."
• "How do you evaluate the trade-offs between object storage and block storage for a globally accessed data archive?"
• "Describe a time you had to optimize a data pipeline that was failing under heavy load."

Cloud and High-Performance Computing (HPC) Infrastructure

AURA operates in a hybrid environment, utilizing both on-premise High-Performance Computing (HPC) clusters and public cloud infrastructure (AWS/GCP). You will be evaluated on your ability to navigate this hybrid landscape. Interviewers want to see that you understand when to leverage the elasticity of the cloud versus the raw compute power of an on-premise cluster. A strong candidate will demonstrate a deep understanding of cost-optimization, containerization, and infrastructure as code.

Be ready to go over:

• Hybrid Architecture – Bridging on-premise data centers with cloud environments securely.
• Containerization and Orchestration – Deploying scalable services using Docker and Kubernetes.
• Infrastructure as Code (IaC) – Managing environments using Terraform or Ansible.
• Advanced concepts (less common) – Designing job schedulers for compute-heavy astronomical data reduction tasks.

Example questions or scenarios:

• "How would you architect a system that requires bursting compute workloads into the cloud during peak observation periods?"
• "Explain your strategy for managing infrastructure costs in a cloud-heavy data processing environment."
• "What are the key security considerations when bridging an on-premise scientific network with AWS?"

Cross-Functional Leadership and Stakeholder Management

As a Solutions Architect, you are the bridge between the engineering teams building the systems and the scientists using them. This area evaluates your soft skills, specifically your ability to gather requirements from non-technical stakeholders, manage expectations, and lead engineering teams through complex implementations. Strong performance involves sharing specific examples of how you achieved consensus among competing priorities and how you handled pushback.

Be ready to go over:

• Requirements Gathering – Translating ambiguous scientific needs into concrete technical specifications.
• Technical Mentorship – Guiding junior and mid-level engineers in best practices.
• Conflict Resolution – Navigating disagreements between product/science goals and engineering constraints.
• Advanced concepts (less common) – Leading architecture review boards or establishing organizational technical standards.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex architectural trade-off to a non-technical stakeholder."
• "How do you handle a situation where a key stakeholder demands a feature that compromises the system's long-term scalability?"
• "Describe your approach to documenting and evangelizing a new architectural standard across multiple engineering teams."

Security, Compliance, and Open Science

A key mandate for AURA is making astronomical data available to the global scientific community while protecting the integrity of the archives and adhering to government funding regulations. You will be evaluated on your understanding of data security, identity and access management (IAM), and data preservation strategies. A strong candidate understands the balance between securing a system and ensuring it remains accessible for "open science."

Be ready to go over:

• Identity and Access Management – Designing secure authentication and authorization for distributed user bases.
• Data Provenance and Preservation – Ensuring data remains accessible and unaltered for decades.
• Disaster Recovery – Architecting multi-region backup and recovery strategies for irreplaceable scientific data.
• Advanced concepts (less common) – Compliance with federal data security standards (e.g., NIST, FISMA) as they apply to NSF-funded projects.

Example questions or scenarios:

• "How would you design an access control system for a petabyte-scale data lake that serves both internal researchers and the public?"
• "Walk me through your approach to designing a disaster recovery plan for an on-premise data archive."
• "What strategies do you use to ensure long-term data integrity and provenance in a distributed system?"

Key Responsibilities

As a Data Systems Architect at AURA, your day-to-day work revolves around designing and guiding the implementation of massive data systems. You will spend a significant portion of your time drafting architecture design documents, evaluating new technologies, and mapping out the technical future of observatory data centers. You are the ultimate technical authority on how data flows from the telescope to the end-user's screen.

Collaboration is a daily requirement. You will work closely with software engineering teams to ensure your architectures are implemented correctly, providing guidance on coding standards, database schema design, and cloud deployments. Simultaneously, you will interface with scientific staff and project managers to understand upcoming observation campaigns and ensure the infrastructure is prepared to handle new data loads.

You will also drive key strategic initiatives, such as migrating legacy on-premise archives to modern hybrid-cloud environments, optimizing data retrieval speeds for global researchers, and establishing organizational standards for infrastructure as code. Your role is highly proactive; you are expected to identify system bottlenecks before they impact scientific operations and propose scalable, cost-effective solutions.

Role Requirements & Qualifications

To be a competitive candidate for the Solutions Architect role at AURA, you need a robust mix of enterprise-grade technical skills and an appreciation for scientific research environments. The ideal candidate has significant experience in large-scale data engineering and distributed systems.

• Must-have skills – Deep expertise in system architecture and distributed systems. Proficiency in cloud platforms (AWS or GCP) and on-premise infrastructure. Strong command of Python or C++. Experience designing high-throughput data pipelines and managing large-scale databases (SQL and NoSQL). Expertise in containerization (Docker, Kubernetes) and Infrastructure as Code (Terraform).
• Experience level – Typically requires 8+ years of experience in software engineering, data engineering, or systems architecture. Prior experience operating at a senior or lead level, making high-stakes architectural decisions, is expected.
• Soft skills – Exceptional written and verbal communication. The ability to advocate for technical best practices while remaining empathetic to the needs of scientific researchers. Strong presentation skills, particularly the ability to defend architectural choices to a panel of peers.
• Nice-to-have skills – Background in astronomy, physics, or another data-heavy scientific discipline. Experience with specialized scientific data formats (e.g., FITS, HDF5). Familiarity with federal compliance standards or working within government-funded research facilities.

Common Interview Questions

The questions below represent the patterns and themes frequently encountered in AURA's technical and architectural interviews. They are not a memorization list, but rather a guide to help you structure your thinking around the scale and complexity of the Data Systems Architect role.

System Design and Data Architecture

This category tests your ability to design robust, scalable, and cost-effective systems capable of handling massive data throughput.

• Design a system to ingest, process, and archive 50TB of raw sensor data daily from a remote location with limited bandwidth.
• How would you architect a search and discovery interface for a petabyte-scale data archive?
• Walk me through how you would migrate a legacy monolithic data processing application to a microservices architecture.
• Describe how you would design a highly available, multi-region storage solution for irreplaceable scientific data.
• What database technologies would you choose for storing complex metadata associated with astronomical observations, and why?

Cloud Infrastructure and DevOps

These questions evaluate your practical knowledge of deploying, managing, and optimizing infrastructure in hybrid environments.

• How do you design an infrastructure-as-code deployment pipeline for a hybrid cloud environment?
• Explain your strategy for optimizing AWS storage and compute costs for a project with fluctuating, unpredictable workloads.
• How would you architect a Kubernetes cluster to handle compute-intensive data reduction jobs?
• Describe a time you had to troubleshoot a severe performance bottleneck in a distributed system.
• What are the key considerations when designing a secure network bridge between an on-premise data center and a public cloud provider?

Leadership and Behavioral

These questions assess your ability to influence teams, manage stakeholders, and navigate the unique culture of a scientific research organization.

• Tell me about a time you had to convince a skeptical team of engineers to adopt a new technology or architectural pattern.
• Describe a situation where the requirements from your scientific stakeholders directly conflicted with engineering best practices. How did you resolve it?
• Walk me through your process for mentoring senior engineers and elevating the technical standards of your team.
• Tell me about a project that failed due to architectural decisions you made. What did you learn?
• How do you balance the need to deliver new features quickly with the necessity of paying down technical debt?

Frequently Asked Questions

Q: How difficult is the interview process, and how much should I prepare? The process is rigorous, particularly the onsite architecture presentation and deep-dive technical rounds. You should expect to spend significant time preparing a solid presentation of your past work and brushing up on large-scale system design principles. Candidates typically spend 2–3 weeks actively preparing.

Q: What differentiates a successful candidate from an average one? Successful candidates demonstrate a pragmatic approach to architecture. They don't just propose the newest, trendiest technologies; they propose solutions that balance cost, maintainability, and the specific needs of scientific researchers. A genuine enthusiasm for AURA's mission also heavily differentiates top candidates.

Q: What is the working culture like at AURA? The culture is highly collaborative, mission-driven, and somewhat academic. Decisions are often made through consensus and peer review rather than top-down mandates. It is an environment that values deep expertise, thorough documentation, and long-term thinking over rapid, break-things-style development.

Q: What are the location expectations for this role? This specific role is based in Boulder, CO, which is a major hub for AURA operations, specifically housing the National Solar Observatory (NSO) headquarters. While there may be hybrid flexibility, candidates should expect a significant presence in the Boulder office to collaborate directly with scientific and engineering teams.

Q: What is the typical timeline from the first interview to an offer? Given the academic and consensus-driven nature of the organization, the process can take anywhere from 4 to 8 weeks. Scheduling the panel presentation, which requires coordinating multiple senior stakeholders, is often the longest step.

Other General Tips

• Master the STAR Method: For behavioral and leadership questions, structure your answers using Situation, Task, Action, and Result. Be highly specific about the Action you took as an architect, rather than what the team did as a whole.
• Emphasize the "Why": When discussing past architectures or proposing new ones, always articulate the trade-offs. Interviewers at AURA care just as much about why you rejected certain options as they do about the option you ultimately chose.
• Connect with the Mission: Take the time to read about AURA's current projects, such as the Daniel K. Inouye Solar Telescope (DKIST) or the Vera C. Rubin Observatory. Weaving this context into your answers shows genuine interest and helps you frame your technical solutions around their actual use cases.
• Prepare for Ambiguity: System design questions will often be intentionally vague. It is your responsibility to ask clarifying questions about data volume, user access patterns, and latency requirements before you start designing.
• Focus on Observability: In remote, automated observatories, knowing when a system fails is as important as the system itself. Always include logging, monitoring, and alerting strategies in your architectural designs.

Summary & Next Steps

Stepping into the Solutions Architect / Data Systems Architect role at the Association Of Universities For Research In Astronomy is an opportunity to leave a lasting mark on the scientific community. You will be building the foundational data systems that power the next generation of astronomical discovery. The challenges are massive—spanning petabytes of data, hybrid infrastructure, and complex stakeholder landscapes—but the work is incredibly rewarding for those who are passionate about scaling technology for the greater good.

The salary module above reflects the compensation range for this Boulder-based role, which sits between $130,000 and$176,000 USD. When interpreting this range, keep in mind that offers will depend heavily on your years of specialized architectural experience, your proficiency with large-scale data systems, and how well you demonstrate leadership capabilities during the panel stages.

To succeed in this interview process, focus your preparation on system design at scale, cost-effective cloud hybrid strategies, and your ability to communicate complex technical trade-offs to non-technical audiences. Approach the interviews as a collaborative whiteboarding session with future colleagues rather than an interrogation. You have the technical foundation required to excel; now it is about demonstrating how you apply that knowledge to solve unique, mission-critical problems. For further insights, continue exploring specialized architecture interview resources on Dataford to refine your system design and presentation skills.