We built our careers making enterprise data reliable. Now we're making the AI that uses it trustworthy.
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What brought this team together
The leaders at Bigeye came from Uber, Datadog, Microsoft, Visa, Cisco, and IBM. They built the pipelines, the platforms, the security architectures that enterprise data teams relied on. They saw, from the inside, where data and governance failures had the most impact on the organizations that depended on them.
That shared background shapes everything at Bigeye. The leadership team has spent their careers working on data quality, governance, and the organizational challenges that make data unreliable at scale. That experience informs every product decision, every customer engagement, and every strategic move the company makes.
"The challenge of delivering Trustworthy AI is massive one. Solving for that is what I've spent my career preparing for."
Eleanor brings more than 20 years leading global SaaS and data companies through the exact transitions enterprises are navigating now. As CEO at Kensu, she led a data observability company through growth, cutting issue resolution times and expanding its enterprise footprint. As CRO at Odaseva, she scaled revenue operations for a Salesforce data protection platform serving regulated enterprise customers. As SVP at TrustArc, she ran data governance and privacy business lines across Europe, the U.S., and APAC, overseeing some of the most complex regulatory environments in the world.
AI trust platform strategy, aligning product direction with enterprise governance requirements and positioning Bigeye as the standard for data and AI reliability in regulated industries.
"We built data observability before the market had a name for it. Bigeye is doing the same thing for AI trust."
Kyle co-founded Bigeye after scaling Uber's data and experimentation platforms during hypergrowth. As a founding data scientist on Uber's experimentation platform, he standardized metrics across thousands of A/B tests, working at a scale where data quality problems weren't inconveniences. They were business risks. He saw that manual pipeline testing didn't scale, then experimented with ML models on data profiles to identify anomalies automatically. That technique became what the industry now calls data observability.
Developing partnerships and prototype capabilities that extend Bigeye's platform into emerging AI governance use cases.
"In every deployment I work on, there's a moment where we show a team something about their data they didn't know. That is what Bigeye is all about."
Egor co-founded Bigeye after a decade at Uber as a Staff Engineer and member of the original data warehouse team, joining in 2014. He built Uber's first SQL bootcamp, helping hundreds of employees work with data more effectively, and bridged business and engineering teams across a platform supporting hundreds of petabytes. He watched data quality issues silently undermine decisions at a scale most organizations never encounter. As Field CTO at Bigeye, he brings that operational experience directly into enterprise customer engagements, working with data leaders on the technical problems that slow down AI initiatives before they start.
Working with enterprise leaders to design data and AI governance strategies that work for their specific environments.
"What we're building has to be technically rigorous enough to handle real enterprise problems and intuitive enough that data teams actually use it. We're building for both."
Rashmi brings deep expertise in cybersecurity, data platforms, observability, and AI to Bigeye's product organization. In leadership roles at SentinelOne, Tanium, and Cisco, she built products from scratch and scaled them into widely adopted platforms, leading global teams across APAC, Israel, Europe, and the U.S. She created the "Path to CPO" podcast and an "AI Product Management" series, both reflecting her commitment to making the craft of product leadership more accessible. She holds an MBA from The Wharton School.
Defining Bigeye's product strategy, translating enterprise requirements into capabilities within our platform.
"The engineering behind AI trust has to be as rigorous as the promise. That's the standard I hold the team to."
Mohamed built Datadog's AI Observability product suite from concept to launch, leading a team of 40+ engineers and applied scientists through one of the fastest product buildouts in Datadog's history. He presented LLM Observability at the Datadog DASH 2024 opening keynote, giving the work a public reference point. Earlier, he led teams at Amazon Alexa AI on large-scale web QA and streaming systems, and at MathWorks on scientific computing tooling. His doctorate in theoretical computer science covers probabilistic cellular automata, fault-tolerant computation, and Kolmogorov complexity.
Leading engineering and applied science across data observability, lineage, sensitive data scanning, governance, and runtime AI policy enforcement.
"Enterprises need to trust Bigeye before Bigeye can help them build AI trust. You can't sell trust and not earn it yourself."
Joan has spent 27 years in cybersecurity, building security programs at companies where the stakes were genuinely high and the complexity was real. As CISO at Sumo Logic, CSO at Auth0, and BISO at Nike Digital, she operated at the intersection of scale, compliance, and speed. She also co-founded a security startup, giving her the perspective of building security from both sides of the table. Her experience spans early-stage companies through global enterprises preparing for major exits.
Ensuring Bigeye's AI trust platform meets the compliance and security requirements of highly regulated enterprise customers.
"Customers need to rely on their data, and my team is here to make sure they can rely on Bigeye."
Tony brings more than 20 years leading customer-facing organizations in SaaS and data infrastructure. As VP of Customer Success at Matillion, he led global Customer Success, Professional Services, and Support for enterprise cloud data adoption, managing a full post-sales motion at the scale that comes with a market-leading product. Earlier, in senior leadership at Ping Identity, he scaled Professional Services through a private equity acquisition and IPO, building the organizational resilience those transitions require. His expertise spans customer lifecycle management, enterprise engagement models, and aligning customer outcomes with durable revenue growth.
Building the customer success motion that helps enterprises move from data reliability pilots to production-grade AI initiatives.
"We're in the moment where the market knows it has an AI trust problem and is still figuring out what the answer looks like. Bigeye is that answer."
Patricia brings more than 20 years scaling global technology, SaaS, and fintech at some of the most demanding companies in the world. At Microsoft, she served as COO for the $14B U.S. SMB segment. At Visa, as SVP of Global Client Services, she turned around a roughly $50M services portfolio and improved NPS by 20 points. At Intel, she led global programs across product and go-to-market. Most recently as CMO at DispatchTrack, she repositioned the company from point solution to broader platform and built its first integrated go-to-market engine from scratch. She holds an MBA from Duke University's Fuqua School of Business.
Building Bigeye's go-to-market engine, aligning product, marketing, and sales development to drive awareness and enterprise pipeline.
"A lot of platforms get enterprises to 'this is promising.' What we're building takes them all the way to 'we can't operate without it.'"
Drew has spent 15+ years scaling enterprise data and software sales in environments where the technology is complex and the stakes are high. At Ataccama, he rose from Account Executive to VP of Sales for North America, delivering 318% revenue growth to $50M+ ARR, contributing more than 60% of global revenue year-over-year, and playing a central role in the Bain Capital investment. Earlier at IBM, he managed marquee accounts generating $13M+ in annual revenue. At Bigeye, he has led some of the strongest revenue quarters in the company's history.
Building the enterprise sales process by translating complex platform capabilities into specific, measurable business outcomes for data-intensive organizations.
Collective experience, applied to a specific problem
We've worked on this problem across a variety of organizations, industries, and layers of the stack.
Making magic with data
Bigeye's values shape how the team builds, makes decisions, and works with customers. We call them MAGIC, and each one is something we hold ourselves to in practice, not just in principle.
Inspire everyone to reach their full potential, driving continuous improvement and a commitment to high standards.
Embrace change and be quick to respond, turning challenges into opportunities with a flexible and proactive approach.
Communicate with authenticity and honesty, fostering a culture of transparency and kindness.
Make informed and strategic decisions that ensure sustainable growth and long-term success.
Put the customer at the core of everything we do, consistently delivering exceptional service and tailored solutions.
Where we're headed
The AI trust problem isn't going to get simpler. As AI moves from pilots into production, governance requirements get harder, compliance expectations get stricter, and the space between what models can do and what organizations can rely on grows wider. That's what this team is here to close.