LinkedIn timelines are full of two-year stints presented as deep expertise. In regulated financial data, that's not expertise. That's a tour.
I've been at Anadolu Hayat Emeklilik for 13 years. Most of that time has been spent inside the same data warehouse, the same actuarial pipelines, the same regulatory reporting flows that the BRSA and SEDDK depend on. People sometimes ask why I haven't moved. The honest answer: the work gets more interesting the longer you stay, not less. And the institutional memory you accumulate is the thing that actually keeps the systems running when something breaks at 2 AM the night before a regulatory deadline.
What Job-Hopping Optimizes For
Moving every two years optimizes for a specific thing: salary jumps and resume keywords. It works. I'm not going to pretend otherwise. If your goal is to maximize total compensation over a five-year window, hopping is mathematically the right move in most markets.
But here's what it doesn't optimize for:
- Understanding why a column is named the way it is
- Knowing which fields the regulator actually checks versus which ones just sit there
- Remembering the 2017 product launch that broke premium calculations for a specific edge case that still shows up in reconciliation reports
- Knowing which business unit will quietly object if you change a join condition
These are not things you can write down in documentation. People try. The documentation always lies, eventually, because the system keeps moving and the documentation doesn't.
The Schema Has a History
Every production data model in a financial institution is a fossil record. Each table reflects a decision someone made under pressure, often with constraints that no longer exist but whose consequences persist.
A concrete example: in our pension contribution data, there's a field that looks redundant if you only see the current state. A new engineer would normalize it away in their first refactor. But that field exists because, years ago, a regulatory change required us to distinguish between two types of contributions that had previously been treated identically. The regulation was later amended, but the historical data still needs that distinction for reports that go back ten years.
If you weren't there when the decision was made, you treat the schema as arbitrary. If you were there, you treat it as a contract with the past.
This is the part that compounds. Year one, you're learning the schema. Year three, you understand it. Year seven, you remember why it is the way it is. Year ten, you're the person other people ask before they touch it.
When the Regulator Calls
The value of institutional memory becomes obvious the moment a regulator sends a question. They don't ask abstract questions. They ask: "For policy type X under product Y, between these dates, why does this aggregate not match this other aggregate?"
The answer is almost never in the data. The answer is in the history. A product was renamed in 2015. A calculation method changed in 2018 with a six-month transition window. A specific customer segment was migrated between two systems and the migration left a known, documented discrepancy that was accepted by the regulator at the time.
A consultant who joined six months ago cannot answer that question. They can investigate, but investigation takes weeks. Someone who was there can answer in an hour. The difference matters because regulators do not enjoy waiting.
Pipeline Compromises That Look Like Mistakes
Every long-running data pipeline has compromises baked in. Joins that look inefficient. Filters that look arbitrary. Hardcoded values that look like bugs.
Most of them aren't bugs. They're scars.
- The hardcoded date filter exists because before that date, the source system stored data in a fundamentally different way and rebuilding history was deemed not worth the cost
- The seemingly redundant outer join exists because one source system occasionally drops records for a specific product line, and the business confirmed they'd rather see nulls than miss rows
- The strange CASE statement exists because of a one-time data correction approved by audit that has to be applied every time the table is rebuilt
A new engineer cleaning this up will break production. A new engineer cleaning this up under pressure to "modernize the stack" will break production catastrophically. I've watched this happen at peer companies, where a year of velocity gains was wiped out in a single quarter of reconciliation failures.
What Depth Actually Buys You
Depth in one company buys you the ability to make confident decisions quickly. That's it. That's the whole thing. But it's enormous.
When someone proposes a change, I can tell within minutes whether it will work, what it will break, and which department will complain. Not because I'm smart, but because I've seen the same shape of proposal before, watched it succeed or fail, and remember why.
Breadth gives you patterns. Depth gives you specifics. In regulated finance, the specifics are what get audited.
Where I'd Push Back On Myself
Staying has costs. I'll admit them.
- You stop seeing your own environment clearly. Things that are obviously broken become invisible because they've always been that way
- You miss exposure to tooling and approaches that other companies have figured out
- Your professional network inside one company is deep but narrow externally
- Your salary trajectory is almost certainly lower than the hopper's
I compensate for the first two by reading aggressively, talking to people outside my company, and being deliberate about bringing in new tools when they actually solve problems. Not when they're trendy.
The network and salary points I accept as the price of doing work I find meaningful in a domain where my accumulated knowledge is genuinely valuable.
The Quiet Argument
The tech industry loves the narrative of the engineer who joined three startups in five years and now runs their own. That's a real path and it produces real value. But it's not the only path, and in regulated industries it's often the wrong one.
The quiet argument for staying is this: some problems can only be solved by someone who remembers. In a domain where the cost of being wrong is a regulatory finding, remembering is a competitive advantage that no amount of breadth can replicate.
Nine years, thirteen years, whatever the number is — it's not stagnation. It's the only way to know certain things at all.