Vantor Innovation & Technology Culture

What is your role on the IT team, and what are your responsibilities?

I currently lead the infrastructure, operations and engineering organization in Maxar IT. We operate Maxar’s datacenters, network, servers, storage, platforms and more. We also partner closely with our colleagues to build and engineer the critical services that power all of the cool technology Maxar’s mission depends on.

How does Maxar support IT professionals? What opportunities for learning and growth are offered? How does your team effectively work together?

Maxar depends on each of its employees keeping up with the rapidly changing world of technology. In IT, we encourage and support all employees to invest in continuing education, both within their area of core competency as well as in adjacent areas. We want to ensure our skill sets continue to evolve to meet the future needs of the business.

Why might someone want to join Maxar Technologies’ IT team? What routines, traditions and perks does your team offer?

Aside from joining a team that supports flying satellites using some of the coolest, state-of-the-art technology and software, and being at the center of so many important world events, our IT team is made up of some of the best and brightest technologists, all of whom are passionate about supporting the company mission. There is no shortage of fascinating and innovative work to be done, and you’ll have a great deal of independence in being able to work on projects that interest you outside of your core area of expertise.

How does your team stay ahead of emerging technology trends while scaling fast?

We process satellite imagery at scale, transforming raw sensor data into intelligence products for customers who need answers fast. Staying ahead means constantly finding ways to reduce latency while maintaining the precision and accuracy our customers depend on. 

Simplicity and portability are core to how we design. Our image processing service runs anywhere. The same codebase in our headquarters can deploy to regional nodes closer to where imagery is collected. Our next-gen Rapid Access Program initiative is fundamentally about processing data in-region to cut delivery times, so we minimize dependencies and keep our footprint small. Simpler systems are more robust, easier to troubleshoot and faster to deploy at the tactical edge. 

Automation is relentless. When we deploy to a new region, automation handles provisioning, deployment and validation. If something requires a human to remember to do it, eventually it won’t get done. We build systems that take care of themselves. 

Open, standard interfaces matter. Our services expose well-defined APIs that allow components, whether they’re built by us, other Vantor teams or partners, to integrate cleanly. When we added support for new sensor types, we extended existing services rather than rebuilding from scratch. 

The payoff of all this is time. When the foundation is solid, there’s time to be curious. We read papers, prototype new approaches, and share what we find across the organization. That’s how you stay ahead; not by chasing every trend, but by building a foundation solid enough that you can afford to experiment. 

What recent product or feature are you most proud of — and what impact has it had?

I’m proud of our image quality and the effort we put into advancing it. Speed and simplicity matter, too, but imagery is only useful if you can trust it, so quality comes first. 

My background is in spatial and radiometric accuracy. Spatial accuracy is about using the best available ancillary data and resampling as few times as possible. Every resample loses information. We obsess over getting pixels to their true ground location while preserving as much of the original signal as we can. 

Radiometric accuracy is about calibration and atmospheric compensation. We tie radiometric calibration to real atmospheric measurements, so pixel values represent actual surface reflectance, not sensor characteristics or atmospheric effects.  

Both matter because this imagery feeds real decisions. Change detection, spectral analysis and object detection: These go to people and ML systems, and the quality of the input determines the quality of the output. 

Recently, I’m proud of bringing this precision to constrained deployment environments. Our next-gen Rapid Access Program initiative moves processing closer to where imagery is collected — smaller footprint and fewer resources. The challenge was achieving simplicity while making breakthrough quality improvements, without settling for incremental gains. 

This was a time when we collaborated deeply across the company, with system engineers, software engineers, quality assurance and ground systems team members all acting like owners. We had relentless design meetings focused on simplicity to hit tight timelines in what was genuinely new territory for us. With good planning, there were minimal late nights. People were excited to do things differently. The thrill was seeing a beautifully accurate image on screen 11 minutes from capture, from a satellite 400 kilometers up. When seconds count, that’s the work that matters. 

How do you create a culture where innovation and experimentation are encouraged daily?

Innovation happens when experimentation is low-risk and learning is valued as much as success. We’ve built a few practices into how we work that make trying new things feel natural. 

First, we make experimentation cheap. Because our system is portable and lightweight, we can spin up realistic test environments quickly. You can try a new approach against real data in a safe environment, gather actual performance numbers, and make informed decisions. No waiting for a release cycle to see if your idea works. 

Second, we question assumptions openly. In design reviews and code reviews, “Why do we do it this way?” is a real question, not criticism. Often, the answer is “Because it made sense when we built it, but things have changed.” These conversations surface opportunities that would otherwise go unnoticed. 

Third, we share what we learn. When an experiment teaches us something — whether it works or not — we share it broadly. This builds trust that the organization values learning, not just wins. Our leadership reinforces this. Our chief technology officer leads by example, approaching problems as a spatial intelligence problem, not just code to write. That means staying curious about how emerging techniques could help our customers. We read papers, prototype ideas, and bring findings back to the team. It’s not formal research and development time; it’s just part of how we work. 

The next-gen Rapid Access Program work showed what this looks like at its best: excellent engineers excited to do things new, a relentless drive to make it faster and the shared satisfaction when it actually worked. That energy doesn’t come from mandates. It comes from giving people room to own the outcome and experiment.