For much of his career, Thakkar has worked in environments where mistakes are expensive and abstractions break quickly. Code does not live safely behind a screen. It moves inventory, controls machinery, and influences decisions worth millions of dollars. That reality has shaped how he thinks about product leadership and why he believes product managers must understand the systems they oversee, not just the users they serve.
His journey did not begin with roadmaps or frameworks. It began with engineering constraints.
As an undergraduate, Thakkar led a team designing a Formula 1 chassis with a single uncompromising objective: reduce weight without sacrificing structural integrity. Cutting mass from 65 kilograms to 39 kilograms was not a matter of preference or iteration. It was a physics problem. The material choices, calculations, and tolerances either worked or they did not.
That experience left a lasting impression. In physical systems, decisions are irreversible and assumptions are punished. Years later, when he entered high growth technology environments, he recognized the same tension playing out at a larger scale. Software moved at speed. Hardware remained bound by reality. Product decisions sat uncomfortably in between.
As Thakkar moved into product roles within complex industrial and automotive ecosystems, he began to see a recurring pattern. Many product failures were not caused by lack of ambition or poor intent. They stemmed from a mismatch between decision makers and the systems they were guiding.
Hardware was treated like software. Timelines assumed reversibility. Constraints were discovered late. Product managers were fluent in user stories but disconnected from supply chains, data pipelines, and physical workflows.
Rather than accepting this as normal, Thakkar leaned deeper into the technical layers of the products he owned. For him, product management was not about translating between teams. It was about understanding the full system well enough to make informed trade offs.
What allowed him to bridge the gap between steel and software was data, but not the simplified metrics often used in digital products. In manufacturing and hardware heavy environments, data is noisy, fragmented, and high stakes. It represents physical reality such as parts, movement, delays, and cost.
During his work on large scale planning systems, Thakkar focused less on dashboards and more on data integrity. Managing databases with tens of thousands of parts, real time data pipelines, and operational dependencies required an engineer’s mindset. The goal was not a visually appealing product. It was a system that reflected reality accurately enough to be trusted.
The impact of this approach was tangible. There were double digit efficiency improvements and multi million dollar inventory savings. These results did not come from feature innovation, but from systems that behaved predictably under pressure.
As his responsibilities expanded, so did his perspective. Thakkar began to think in terms of systems rather than applications. A change in one layer such as software, supply chain, or human process inevitably affected others.
This systems thinking became central to his work on capacity planning, risk identification, and operational automation. Adoption metrics mattered, but what mattered more was what adoption revealed. Hidden bottlenecks, latent risks, and fragile dependencies surfaced through real use. Where others celebrated usage, he focused on what usage exposed.
His academic work further reinforced this outlook. Studying project and systems management formalized what experience had already taught him. Optimizing isolated components without understanding the whole often creates new failure modes elsewhere.
Despite common stereotypes, Thakkar’s technical depth did not distance him from stakeholders. It strengthened his credibility. Engineers trusted him because he understood constraints. Data teams trusted him because he understood pipelines. Business leaders trusted him because he could explain risk in concrete terms.
When building cross functional platforms, progress depended less on persuasion and more on technical clarity. Integration challenges were solved through understanding data models, security concerns, and operational realities. Authority came from competence, not position.
Today, Thakkar represents a different model of product leadership rooted in engineering literacy, data fluency, and systems thinking. His belief is simple but demanding. Product managers working across hardware and software environments must understand how things work, not just what they are supposed to do.
He often advises aspiring product leaders to resist purely managerial paths. Learn SQL. Understand how data flows through systems. Spend time on factory floors. Watch how decisions propagate across operations. Only then can a product manager truly lead.
In an era where products increasingly shape the physical world, Thakkar’s journey reflects a broader shift. Product management is no longer about managing interfaces. It is about engineering outcomes.
And in that world, knowing where and how to apply technology matters far more than talking about it.