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May 7, 2024

Meet Bigeye’s New VPs: How Stewart Plumb and Tony Peck are Shaping Bigeye’s Future

Our new executive appointments, Stewart Plumb and Tony Peck, are set to transform Bigeye’s approach to data observability and customer success.

Kyle Kirwan
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We're excited to expand our executive team with two important new hires. Stewart Plumb is joining us as the Vice President of Sales, and Tony Peck as the Vice President of Customer Success. These appointments go beyond just growing our team—they are strategic steps to strengthen our commitment to providing excellent data reliability solutions.

Strengthening Sales with Stewart Plumb

Stewart Plumb brings over 25 years of experience in sales and leadership in the data observability space, with a recent tenure at Anomalo and previous roles at Immuta, Trifacta, BP Capital, and arcplan. Stewart is well-known for his ability to drive revenue growth and lead effective teams.

"Stepping into the role of Sales Leader with a vision for the future, I am committed to empowering our clients by providing them with cutting-edge data observability solutions," said Stewart Plumb. "Together, we are setting new standards for excellence and innovation in our industry."

Stewart’s leadership is set to not only grow our sales initiatives but also improve our ability to meet the needs of enterprise businesses seeking reliable data insights.

Boosting Customer Success with Tony Peck

Tony Peck has a proven track record of leading customer-facing teams and improving customer engagement and satisfaction. As the former Vice President of Customer Success at Matillion, he led initiatives that significantly enhanced customer support and success.

"I am thrilled to be joining the talented team at Bigeye at this exciting point in the evolution of the company," Tony says. "I look forward to working closely with our internal teams and external partners in providing the programs and resources needed to support our growing customer base."

Tony's role will be crucial in ensuring that our customers have the best possible experience and gain maximum value from our products.

Looking Ahead

“Tony and Stewart both bring incredibly deep experience in the data space and a strong track record of delivering value for enterprise customers,” said Kyle Kirwan, CEO of Bigeye. "They’re the right leaders to help us enhance our services for the enterprise segment. I can’t wait to see the impact they’ll have."

By adding Stewart and Tony to our team, we're reinforcing our commitment to leading the way in data observability and delivering reliable, innovative solutions to our clients. Their expertise will be vital as we continue to innovate and grow.

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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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