In Part 1, we traced careers in their own time, advancing by scope, accountability, and outcomes rather than years. That was the traveler’s view: motion measured by impact, not tenure.
Now we switch to the observer’s frame. In general relativity, massive bodies bend spacetime and change how time flows nearby. Companies do something similar. Each firm has “mass” (size, structure) and a market “velocity” (operating tempo). Together, they reshape how careers progress and how seniority is perceived.
Let's get Curioz and hypothesize: a company is a moving observer whose mass bends career time. As mass increases, the local clock slows, seniority may take longer to appear. As mass decreases, the clock is likely to compress and titles arrive sooner. Motion modulates this curve: a faster operating tempo can compress cycles even at high mass, while a slower tempo can stretch them even at modest mass.
And so, with our instruments tuned and our frame defined, the experiment begins.
Having explored how organizations might bend the meaning of seniority, we turned our Curioz lenses toward company scale. This time, we focused on data roles, the disciplines where skills, tooling, and business integration turn information into outcomes. Our hypothesis is simple and provisional: size might change how quickly professionals reach seniority. So we tested it. Using Curioz data for 2025, we examined how experience thresholds vary from startups to enterprises across Data Scientists, Data Engineers, Data Architects, and Data Analysts.
We kept the parameters from the prior experiment, anchoring on the midpoint rather than the average. For each role labeled “Senior,” we marked three time-based ticks, like a dial:
Minimum: the entry point where companies commonly mark “Senior”, the entry tick.
Midpoint: the market center where half of hires actually land, reflecting better the peg for compensation and autonomy; this shifts with company scale and resists outliers better than the mean.
Maximum: the far edge of the senior dial, beyond which titles reach exit velocity towards Lead, Staff, Principal, or Expert.
Methodology: Curioz groups companies by size to compare how scale influences career progression: Micro (1–10), Small (11–50), Medium (51–250), Large (251–1000), and Enterprise (1001–5000). This segmentation helps reveal how company growth reshapes visibility, ownership, and the pace at which professionals reach seniority.
First up in our lens is the Data Scientist. Different firm scales warp the path, yet the gravitational pull remains clear. Careers arc toward 7 years before settling into seniority, where skill, practice, and domain context reach equilibrium. Startups tighten the orbit through accelerated ownership, enterprises and large companies widen it with structure, and mid-market paths sit between those forces.
Across company sizes, entry into seniority begins around 5.5 years, with micro firms slightly earlier and mid-market to enterprise firms clustering tightly around that mark. The median inflection point centers near 7 years, confirming a stable gravitational midpoint. Exit velocity peaks between 7.9 and 8.3 years, where most professionals stabilize as senior or advance into staff and lead-level trajectories.
On the other hand, Data Engineers enter seniority around 5 years, tightening in micro and small firms where ownership spans the full stack, with enterprise starting a bit later. The median inflection rises with scale, from 5.1(micro/small) to 6.3 (enterprise), signaling how structure and specialization extend development time. Exit from the seniority orbit falls between 6.1 and 7.5 years. The arc lengthens overall with size, though there’s minor mid-market variation (Small 6.7 > Medium 6.5 ≈ Large 6.6), and the bands overlap, so firm context matters as much as headcount.
Exit from the seniority orbit appears between 6.1 and 7.5 years, with larger environments lengthening the trajectory through governance, tooling, and cross-platform integration. Smaller teams reach velocity faster; enterprises sustain the journey longer, yet both converge toward mastery built on reliability, optimization, and scale. The difference is in how Data Engineers work unfolds. Smaller teams compress cycles, own pipelines end to end, and ship often, so learning compounds quickly. Larger settings widen scope and deepen specialization; experience must span platforms, governance, and collaboration before it resolves into seniority. The chart is clear: for Data Engineers, gravity is scale.
Across scales, entry into seniority for Data Architects begins near 5.5 years, tightening between micro and mid-sized firms. The midpoint rises steadily with company mass, from about 6 in smaller firms to 8.2 in enterprises, showing how integration, governance, and system alignment expand the journey. Exit spans 7 to 9 years, with smaller companies completing the orbit faster and large ecosystems demanding longer durability.
The gap likely stems from enterprise architecture compounding complexity, more integrations, stricter governance and risk controls, wider stakeholder alignment, and slower iteration. Each layer adds friction and raises the burden of demonstrated stability. In small firms, impact is direct and cycles are short; in large firms, endurance becomes the test. Only after experience accumulates across domains, standards, and risk does “stability” truly qualify in the enterprise.
Lastly, entry into seniority for Data Analysts begins around 4.5 to 5 years, with the median spanning 5 to 5.6 years and exit between 5.9 and 6.5 years. The range is notably narrow, evidence that analytical skill transfers cleanly across environments. Smaller firms see slightly faster acceleration, while enterprises stretch timelines modestly through process scale and layered validation.
We suspect this pattern holds in many organizations because analyst work compounds more through iteration, rather than integration. Dependency chains are short, feedback loops are fast, and tools are largely standardized across firms, so experience travels well. Governance is typically lighter, and results are validated by business impact rather than long technical dependencies. As companies scale, analytical fluency, experimentation, and communication tend to scale with them, keeping the orbit relatively stable.
Our Curioz hypothesis holds: company mass bends career time. Bigger structures slow lift-off through coordination and governance; smaller ones speed it up with proximity and ownership. Scale doesn’t just change scope, it changes time.
The pull also depends on role complexity. The more a discipline intertwines with systems, governance, or cross-team dependencies, the stronger the drag. Data Architects feel it most: integration and risk stretch their orbits as companies grow. Data Engineers experience a moderate pull as structure and specialization deepen. Data Scientists move in balanced gravity, steady across scales but slower at the top end. Data Analysts feel the lightest tug; their tools and feedback loops stay portable, keeping timelines tight even in heavier environments.
Yet we remain Curioz! We suspect that company gravity is shaped not only by mass and complexity, but also by other forces, industry, headquarters location, local labor markets, and organizational culture, each adding its own curvature to career time. Our instruments stay out of the cabinet; the exploration continues.
In other words, every company carries its own gravity well strong enough to warp the experience of time for those who work within its field.
NOTE: This post is based on research by Inuits.it and Curioz.io, and has been crossposted on both platforms.