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October 3, 2025

Life at Bigeye: Alex Nouhan, Customer Success Engineer

4 min read

Alex Nouhan
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Alex Nouhan's path to Bigeye makes perfect sense once you know he used to be pit crew for a rally racing team. "We supported a 1998 Subaru Impreza that raced independently for a few seasons," he says, lighting up at the memory. "It was such a fun, but hectic, experience!" There's something about that combination of technical precision, teamwork under pressure, and problem-solving on the fly that applies to everything he does, including his current role as Bigeye's Customer Success Engineer. 

Alex's Path To Bigeye

Alex spent years in the automotive industry under what he calls "classical engineering guidelines," which gave him valuable structure but left him feeling constrained. "I'm thankful for the approach that the regimented industry lent to my professional development, but the goalposts and limitations of material-parts-dependent industries didn't create the most exciting environment for me."

When he finally made the leap to SaaS, everything changed. "I felt as though the possibilities were endless," he explains. "There are opportunities for innovation and optimization at every turn." So when Diane reached out last fall about Bigeye's approach to data observability, Alex was immediately interested. As someone obsessed with customer journeys, he could see the obvious value. "Bigeye represented an opportunity to partner with teams that absolutely need visibility into their data pipelines."

Meeting the team sealed the deal. "I was introduced to a group that is working hard on novel problems in a space that is teeming with potential." Ten months later, he's still energized by that same sense of possibility.

Alex playing on stage, with his Bigeye co-workers, and exploring the great outdoors.

His Day to Day

As Bigeye's Customer Success Engineer, Alex's days revolve around what he describes as walking alongside customers toward success. "I strive to meet teams in whatever state or stage they are and walk alongside them towards success," he says. Whether he's leading day-one training or helping teams envision their data observability goals, his approach is deeply collaborative.

One project he's particularly excited about is something called the bigIQ informational video series. "The Customer Success team and I have been crafting a series of videos that are designed to guide Bigeye users through the features and facets of Bigeye, punctuate observability milestones, and act as a companion to our documentation." It's the kind of scalable resource that helps customers get up to speed on data observability, fast. 

What strikes him most about Bigeye is the alignment. "The Bigeye team is a group of driven professionals and every one of them seems to have their eyes on the prize," he says. "I felt welcomed and included right from the start and have been pleasantly surprised over and over again by the truly exceptional group of people here."

He's quick to credit teammates like Larissa, Zach, Christian, and Andy for showing him the ropes, and gives special thanks to his manager, Tony for "contributing greatly to my personal definition of professionalism."

Life Outside Bigeye

Alex's mornings are ritualistic in the best way. He starts with stretches and calisthenics in the sun, then coffee (and he's serious about his coffee.) "I'm nothing without a cup of coffee," he laughs. "A good cup of coffee, or better yet, a well-crafted latte, cappuccino, or cortado puts me in a good mood." He's recently gotten into Life Extension brand coffee prepared via percolator, and swears that first cup changes his whole day.

After coffee, he takes another walk outside to check on trees and wildlife. "I love being outdoors!" he says. 

When it comes to recharging after work, Alex has what he admits are "several hobbies that some might call eccentric"-- playing in a band, racing cars, and making independent films. But what really gives him energy is something much simpler: "hanging out with my 5 chickens!" He and his fiancée raised them from hatchlings, and now they have what he describes as "a wonderful flock of curious and very hungry chickens. They produce a few eggs per day, but are far more valuable for their cheeky personalities and the joy that they bring us." 

His weekends are spent gardening, tackling home improvement projects, or doing what he considers the perfect way to unwind: "sitting outside, in the setting sun, and strumming a guitar." His go-to meal is specific: "Tacos! 1 carne asada, 1 carnitas, 1 lengua, and a tamarind Jarritos." 

Alex and his fiancée, beloved chickens and hanging with a falcon.

Alex has always been drawn to teams tackling complex challenges. At Bigeye, he's found that perfect combination of innovation, customer impact, and the kind of people who make the work feel like something bigger than just a job. "I'm so glad that I embarked on a career journey into modern tech," he reflects, "and it is getting more exciting by the minute at Bigeye."

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Resource
Monthly cost ($)
Number of resources
Time (months)
Total cost ($)
Software/Data engineer
$15,000
3
12
$540,000
Data analyst
$12,000
2
6
$144,000
Business analyst
$10,000
1
3
$30,000
Data/product manager
$20,000
2
6
$240,000
Total cost
$954,000
Role
Goals
Common needs
Data engineers
Overall data flow. Data is fresh and operating at full volume. Jobs are always running, so data outages don't impact downstream systems.
Freshness + volume
Monitoring
Schema change detection
Lineage monitoring
Data scientists
Specific datasets in great detail. Looking for outliers, duplication, and other—sometimes subtle—issues that could affect their analysis or machine learning models.
Freshness monitoringCompleteness monitoringDuplicate detectionOutlier detectionDistribution shift detectionDimensional slicing and dicing
Analytics engineers
Rapidly testing the changes they’re making within the data model. Move fast and not break things—without spending hours writing tons of pipeline tests.
Lineage monitoringETL blue/green testing
Business intelligence analysts
The business impact of data. Understand where they should spend their time digging in, and when they have a red herring caused by a data pipeline problem.
Integration with analytics toolsAnomaly detectionCustom business metricsDimensional slicing and dicing
Other stakeholders
Data reliability. Customers and stakeholders don’t want data issues to bog them down, delay deadlines, or provide inaccurate information.
Integration with analytics toolsReporting and insights

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