EXPOSED
Most of the day is schema design, SQL/DDL authoring, query tuning, ETL mapping and documentation — all things LLMs already produce at usable quality for standard patterns, which compresses the routine tier of this job hard. What persists is ownership of consequential, ambiguous calls: data model tradeoffs that lock in a decade of application behavior, partitioning and capacity strategy under real load, migration cutovers where a mistake corrupts the business record of truth, and compliance posture for regulated data. There is no license and no signature requirement, so the moat is accountability and organizational trust rather than regulation.
Headcount grew steadily across the period.
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing.
BLS projection, 2024–2034
+8.7% 66,900 → 72,700 on the projections basis
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +8.7% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~4,000 openings a year on average, including replacing people who leave.
DeveloperData MinerData AnalystData ManagerData ModelerData OfficerData EngineerData ArchitectCloud ArchitectData SpecialistDatabase AnalystDatabase ManagerDatabase ModelerServer DeveloperStorage EngineerAnalytics ManagerBig Data EngineerDatabase DesignerDatabase EngineerBig Data ArchitectComputer ArchitectDatabase ArchitectDatabase DeveloperSolution Architect
Analytics Engineer
Holding it up: judgment & accountability . Weakest point: embodiment .
Mixed — a routine tier and a judgment tier Normalization to 3NF, index selection, writing DDL and stored procedures, dimensional modeling for a warehouse, and drafting data dictionaries are all pattern work a model reproduces from a requirements paragraph — what sits above 6 is the un-writable part: reverse-engineering an undocumented legacy schema where column names lie, negotiating with three application teams over who owns the customer record, and sequencing a zero-downtime migration against a live OLTP system.
Fully desk- and screen-based The 2 is for the rare rack-and-console day — standing up a cluster in a colo, sizing physical storage, or being in the datacenter during a cutover window — because everything else is DBeaver, dbt, Terraform and a Zoom call.
No licence, no signature requirement No state licenses database architects and no statute requires a named human to sign off a schema change; the 4 reflects only the practical gatekeeping of vendor certifications (Oracle OCP, AWS/Azure data credentials) and SOX/HIPAA change-control records that put your name on the approval ticket without putting you personally on the hook.
Exists to be accountable for ambiguous calls A 15 is warranted because the calls you own are irreversible at business scale: choosing a sharding key or surrogate-key strategy that application code will assume for a decade, deciding whether a migration cutover proceeds or rolls back at 3am with the record of truth in flight, and setting encryption, masking and retention posture for regulated data where the wrong choice becomes a breach report rather than a bug ticket.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (9/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (4/20) is whether the law requires a licensed human to sign. Trust premium (8/20) is whether buyers specifically pay for a person. Judgment and accountability (15/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 27 of this occupation's 38 points (71%).
Embodiment (2/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
No occupation passed every test: close enough to database architects on skills and subject matter, at least 10 points more resistant, no big jump in training, no new licence, no pay cut, and not shrinking on its own. That happens for 223 of the 654 occupations here that aren't SAFE, and it is worth stating plainly rather than leaving the section off.
The usual reason is that exposure travels with the skill profile. The jobs most similar to yours tend to be exposed for the same reasons yours is, so the near neighbours don't clear the gap — and the ones that do are a different kind of work, not a transfer of what you already know. Read that as a limit of this method, not a verdict that you're stuck: it only compares whole occupations, and it cannot see specialisation, industry, or anything you'd bring that isn't in a federal skill survey.
Here is that claim on your own job rather than in the abstract. These are the three occupations closest to this one by skill and subject matter — the places the work would most naturally transfer — with what the register scores them:
That is the whole problem in three lines. The nearest work is not meaningfully safer, so there is no move here that trades a similar skill set for a better verdict. This is not us running out of ideas — it is what the neighbourhood looks like.
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 48/100, still EXPOSED.
Task-mix shift is genuine here: if LLM-generated DDL and query tuning absorb the routine tier, the residual role concentrates in migration cutover planning, capacity/partitioning strategy under real load, and reconciling conflicting data models across acquired systems — work that requires knowing undocumented production behavior no model has access to. Watch for job postings retitled toward 'data platform owner' / 'migration lead' with schema authoring dropped from the requirements.
Regulated-data attestation regimes are the only realistic route: e.g. if bank regulators' data lineage expectations (BCBS 239, already enforced through Fed/OCC MRAs) or an SEC cyber-disclosure follow-on start requiring a named individual to attest that a production data model and its lineage documentation are accurate, that signature typically lands on the data architect. Similarly, HIPAA de-identification determinations under the expert-determination method already require a named qualified person; extending that to a named architect for each de-identified data mart would create a real sign-off.
Already near ceiling. It rises further only if organizations formalize schema/migration approval as a gated change-authority role — e.g. an internal data governance board where a named architect must approve irreversible migrations, mirroring how change advisory boards work in SOX-scoped environments. Auditable named approval, not just informal ownership.
The limit. There is no plausible route to a higher trust premium: buyers of database work purchase working systems, not human authorship, and no client segment pays extra for hand-written DDL. Embodiment cannot move. Even with the liability lever, this occupation's realistic ceiling is roughly the high 40s — the licensure and signature infrastructure that protects engineers and accountants does not exist for data architecture and no professional body (there is no equivalent of a state PE board here) is building it.
| New York-Newark-Jersey City, NY-NJ | 5,460 | $131,540 -6% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 4,740 | $162,870 +17% |
| Dallas-Fort Worth-Arlington, TX | 4,320 | $154,770 +11% |
| Seattle-Tacoma-Bellevue, WA | 2,500 | $105,430 -24% |
| Boston-Cambridge-Newton, MA-NH | 2,120 | $161,650 +16% |
| Atlanta-Sandy Springs-Roswell, GA | 2,110 | $139,500 +0% |
| San Francisco-Oakland-Fremont, CA | 1,600 | $175,860 +26% |
| Charlotte-Concord-Gastonia, NC-SC | 1,560 | $138,870 +0% |
| San Jose-Sunnyvale-Santa Clara, CA | 1,120 | $197,960 +42% |
| San Francisco-Oakland-Fremont, CA | 1,600 | $175,860 +26% |
| Reno, NV | 80 | $170,390 +22% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 38. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.