There’s a point where data engineering stops being about pipelines and starts shaping how a business runs. At Equinix, that point comes early.
Across teams in India, the U.S. and Singapore, data engineers aren’t working on isolated systems. They’re building trusted data products that power decisions across the business, from how customer bookings flow through to how revenue is recognized. We speak with Radhakrishnan Ayyappanaicker, Director, IT, on our Digital Chief Data and Analytics team to learn more about the data challenges his team solves, the impact of their work, and the role of data engineering in powering business decisions at Equinix.
What data engineering work looks like at Equinix
“We’re constantly adapting data insights into real business use cases at scale,” says Radhakrishnan.
At a practical level, that means bringing together data that was never designed to work together. Systems evolve over time. Definitions vary. Data arrives at different speeds and levels of quality.
The job is to make sense of all of it.
Engineers build and maintain cloud-scale data platforms that ingest data from hundreds of operational systems and make it available for analytics and business decision-making. The data platforms the team builds are also increasingly supporting AI, machine learning, and advanced analytics initiatives across the business. But the work does not stop there. Raw data is not enough. It must be usable, consistent, and trusted.
“The goal is to make it usable, reliable, and actionable at scale.”
That means turning fragmented data into usable data products, adapting insights into real-world use cases, and building systems that continue to perform as the business grows.
A good example is the “book to bill” data product, which supports how Equinix tracks the life cycle of a customer booking all the way through to revenue. It sounds straightforward, but behind it are multiple systems, dependencies, and edge cases that need to be aligned.
Engineering decisions shape how data is modelled, how pipelines are structured, and how quality is maintained. These choices directly affect how accurately the business sees itself.
Scale and complexity you can’t replicate easily
“We’re dealing with global-scale data and complex systems that directly impact how the business operates,” Radhakrishnan says. Equinix operates one of the world’s largest digital infrastructure footprints, creating unique opportunities to solve data challenges that span geographies, customers, products, and operational environments.
Data engineers design for:
- Distributed, cross-region systems
- High availability and resilience (think 99.9999%+ reliability standards)
- Continuous performance under peak business demand
Ownership and collaboration
Radhakrishnan shares that at Equinix, “We create an environment where people can take ownership, lead initiatives, and continue learning.”
Engineers are encouraged to take on problems that stretch them, whether that’s leading a new data product, improving an existing system, or working across teams to solve a larger challenge. You're not simply handed a ticket and told what to build. Engineers are expected to understand the business problem, work with different teams, and help shape the solution from the start.
“We look for people who can bring clarity to complex business requirements,” says Radhakrishnan.
A lean core team often works through the more complex problem spaces, while broader teams focus on building and scaling solutions. This means engineers can influence decisions as well as build solutions.
“My role,” he says, “is to provide clarity, prioritization, trust, and accountability, and to remove friction so the team can focus.” In a fast-moving environment where priorities shift and business needs evolve, that means helping teams navigate complexity and competing demands, and stay focused on what matters most. As he puts it, “During high pressure periods, it’s about guiding the team and making sure they have what they need to deliver.”
Learning is part of how the team operates. Engineers share knowledge, review designs, and reflect on what worked and what did not after delivery. As data engineering continues to evolve, the team is constantly looking for ways to improve how work gets done without compromising quality.
Building the next generation of data products
“We’re constantly looking at how we can make data more accessible, trusted, and actionable across the organization,” Radhakrishnan says.
That means continuing to modernize data platforms, improve data quality, and create scalable data products that can support an increasingly diverse set of business needs. The platforms the team builds today support reporting, analytics, and business intelligence, while also providing the trusted data foundations needed for machine learning and AI initiatives across Equinix.
For engineers, this creates opportunities to solve challenges around scale, performance, reliability, and governance while influencing how enterprise data platforms evolve over time.
Who thrives here
"You need to enjoy solving complex data problems at scale and care deeply about data quality,” Radhakrishnan explains.
The strongest data engineers are curious about how the business works, how people use the data they're building, and how their work influences decisions across the company. They also enjoy working across teams and disciplines. Data products are rarely built by one person alone, and much of the job involves partnering with teams across the business. Technical skills matter, but so does a willingness to learn, ask questions, and take on new challenges.
The data products this team builds help other teams understand customers, monitor performance, and make better decisions every day. At Equinix, data engineering is helping shape how the business runs.
If you're looking to solve complex data challenges at global scale, build trusted data products that support critical business decisions, and help shape the future of enterprise data and AI, explore opportunities with the Equinix software engineering team.