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Key statistics

The identity block — every fact is canon, none is generated.

3 of 8

Track

Management

Family rungs priced

M1 · M2 · M3 · M4 · M5 · M6

11-0000

industrial_energy_operations

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Manages the operations team and daily production activities at a power generation or mining plant. At the Senior Manager (M3) level, the role owns multiple teams or a sub-function, multi-team execution and resourcing trade-offs, and drives sub-function outcomes. Manages managers and/or several teams.

What this level does

Manage day-to-day operations of a plant or major processing unit, owning production goals, operating budget and staffing across shifts
Lead functional operations teams and evaluate operating trends from DCS/SCADA data to keep the plant within environmental and safety limits
Manage the development and rollout of operations policies, procedures and training standards across the plant
Coordinate with engineering, maintenance and safety departments to optimize processes and resolve diverse operational issues
Own compliance with EPA, OSHA and DOT standards for the plant, and drive cost-per-unit output improvement

Knowledge applied: Applies broad operations knowledge to run a plant or unit, evaluating DCS/SCADA trends and owning operating budget and staffing.
Complexity & problem solving: Addresses diverse operational issues and evaluates trends to keep the plant within cost, safety and environmental limits.
Collaboration & interaction: Leads functional operations teams and coordinates with engineering, maintenance and safety departments.
Typical experience: 5–7+ years managing operating professionals and budgets.

Skills at this level

Power/processing plant operations: — Directs the control and operation of power-generating or mineral-processing equipment — boilers, turbines, generators, reactors, water treatment and auxiliaries — via control boards and floor operation to meet production goals safely.
DCS/SCADA control-room supervision: — Oversees monitoring of plant equipment from the DCS and on the plant floor, ensuring operators take corrective action on readings and maintain environmental and safety compliance.
Abnormal-condition and emergency response: — Leads rapid response to abnormal plant conditions, equipment failures and operational issues, restoring safe operation and escalating per procedure.
Environmental and safety regulatory compliance: — Ensures plant operations comply with EPA, OSHA, DOT and environmental standards, enforcing regulatory/corrective action at established monitoring intervals.
Operations policy and training development: — Develops, defines and trains overall operations policies and procedures, and drives operators to achieve required operator and maintenance qualifications per training standards.
Mechanical, electrical and instrumentation/controls competence: — Applies related mechanical, electrical or instrument-and-controls experience within industrial process, electric utility, mining or heavy-construction settings to run and troubleshoot plant equipment.
Preventative maintenance coordination: — Coordinates hands-on inspection, preventative maintenance and repair of turbines, generators, water treatment and auxiliary equipment to keep assets available.
Production records and output optimization: — Keeps accurate daily records of plant processing equipment and uses them to sustain optimal production levels and cost-effective output.
Coaching/mentoring: — Identifies developmental needs and mentors operators advancing toward supervisory and specialist roles.
Cross-functional operations coordination: — Coordinates energy production and distribution operations across engineering, maintenance and safety departments to optimize processes and meet production goals.
Industrial communication protocols: — Applies knowledge of industrial protocols such as Modbus, DNP3, Ethernet, ControlNet, DeviceNet and OPC to oversee DCS/SCADA-connected plant equipment.
Multi-site operations leadership: — Oversees day-to-day operations of assigned locations, supervising site managers and department managers and ensuring compliance across the portfolio.

Model-authored from 22 retrieved sources, then adversarially reviewed by an independent judge panel and held to a minimum-evidence floor before publication. Distinct from corpus-extracted content, which is labelled as such.

Pay across the ladder

Plant Operations (Energy/Mining) — median with , by . Real canon data.

National-base pricing — this family's function differential is pending review, so dollar levels are not function-adjusted (level-over-level % is unaffected).

Pay figures are modeled estimates from our market equation — level-differentiated, with honest-wide bands. They refresh as new leveling evidence lands; treat per-role dollars as directional.

Demand — is the field growing, or just turning over?

SOC 11-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

+26.2%Industrial Production Managers · United States

185,790 → 234,380 jobs

17,100 openings / year, split into:

replacement / exits 16,640

Growth — new jobs (3%)Exits — backfill (97%)

3% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +1.9%
Growth share of hiring: 3%
Exit / retention pressure high: 97%
Openings / year: 17,100

Two quantities, kept apart: observed = employment growth differenced from two BLS OEWS vintages (what already happened); projected = BLS Employment Projections 2024–34, openings decomposed into growth + exits (growth derived from projected employment change). Neither is a pay figure.

Where this role sits — growth vs. pay

level M3 held

Demand axis is projected growth (not raw openings — that just measures churn). A curated, de-duplicated set of recognizable occupations held at this level; this role is highlighted.

Where it pays — by metro

OEWS metro median

San Jose-Sunnyvale-Santa Clara, CA$207k

San Francisco-Oakland-Fremont, CA$186k

Washington-Arlington-Alexandria, DC-VA-MD-WV$172k

Boulder, CO$169k

Seattle-Tacoma-Bellevue, WA$169k

Boston-Cambridge-Newton, MA-NH$162k

Lexington Park, MD$158k

Trenton-Princeton, NJ$157k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $124k; the demand-cube initiative will add growth-by-metro.

What changes across the ladder

Level-distinction canon isn't available for the adjacent levels of this profile yet.

Level differentiators

What separates M3 (M3 — Senior Manager) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Multiple teams or a sub-function
Autonomy: Sets goals within functional strategy
Complexity: Multi-team execution and resourcing trade-offs
Impact: Sub-function outcomes
Decision rights: Owns goals, budget input, and people decisions across teams
Leadership: Manages managers and/or several teams

Full level-by-level matrix (side by side across the ladder) lives on the Ladder drill-down — not here.

Promotion

The next rung from M3 is M4.

Performance criteria for this profile render in the assessment kit →

People also watch

Nearest coordinate neighbors — same , same level. Each row's (confident / review / no match) comes from its — computed, not curated.

Profile	Key	Median	Δ structural
Chief Plant Operations Officer	jf_plant_operations_energy_mining::E3	$352,063†	confident0.06
Plant Operator	jf_plant_operations_energy_mining::P3	$82,468†	confident0.06
Senior Plant Operator	jf_plant_operations_energy_mining::S3	$49,566†	confident0.06
Senior Plant Operations Supervisor	jf_plant_operations_energy_mining::M2	$110,455†	confident0.06

†National-base pricing — this family's function differential is pending review, so the dollar level is not function-adjusted.

Capability cluster

O*NET 11-3051

Core to this role. Click any element with an ⓘ for its O*NET definition. A job-specific capability model (SkillFrame) will sharpen this and add worked examples.

The Job Swatch

M3

jf_plant_operations_energy_mining::M3

Plant Operations Manager

Plant Operations (Energy/Mining)

The swatch is JobFrame's core primitive: a hueset deterministically by the role's superfunction, the profileKey as its color code, the level ladder, and a content fingerprint. Two swatches that look alike are alike.

Because every role carries computable coordinates, similarity, leveling, and pricing become measurable instead of arguable— the whole point of "Pantone for jobs."

Structural

structural@1

Where it sits— position in the work taxonomy + level (weighted Gower, level > function > focus).

Track: M
Superfunction: industrial_energy_operations
Function: plant_operations
Family: plant_operations_energy_mining
Universal level: 3 / 8

Content

content@2

What it's about— a fingerprint over the role's tasks & capabilities.

Two roles can sit far apart structurally yet be close in content — that gap is itself a signal.

3-state matching

How any two swatches compare — a real distance in coordinate space, resolved to a calibrated verdict (never a fake "94% match").

Mining Processing Plant · M3review0.17

Nuclear Power Plant · M3review0.17

Plant Maintenance (Energy/Mining) · M3review0.17

Power Plant Management · M3review0.17

Refinery · M3review0.17

Renewables Plant · M3review0.17

Editions — frozen & versioned

structural@1live

Released 2026-06-17

content@2live

Released 2026-07-28

pay@2live

Released 2026-07-02

semantic@1live

Released 2026-07-02

Coordinates belong to a frozen, versioned edition — a coordinate computed today still means the same thing next quarter. Re-fitting issues a new edition, so comparisons are never silently redefined.

How to read this profile

Every number here is traceable — to a named source, a stated method, and an as-of date — and each value is labeled by how we know it: observed from data, computed by a model, projected forward, or governed canon. Nothing is a black box, and nothing is invented to fill a gap.

Where every number comes from

What	Source & method	Edition	State
Median pay + range	JobFrame pay equation (function base × family multiplier × level × focus differential)	pay@2	computed
Pay across the ladder	Same equation applied at each universal level	pay@2	computed
Demand — growth & openings	BLS Employment Projections; openings decomposed into growth + exits	EP 2024–34	projected
Pay by metro	BLS OEWS metropolitan medians	OEWS 2024	observed
Level distinctions	JobFrame universal-level canon (scope · autonomy · complexity · impact · decision rights · leadership)	canon	canon
Capability cluster	O*NET Knowledge / Skills / Abilities	O*NET 11-3051	computed
Coordinates	JobFrame coordinate system — structural (weighted Gower) + content fingerprint	structural@1 · content@2 · pay@2 · semantic@1	computed
Related roles	Nearest neighbors in the structural coordinate space	structural@1	computed

The principles behind the numbers

Ranges, not false precision

Pay is P10 · median · P90, never a single misleading number.

Three honest states, never a fake %

Role matches resolve to confident / review / no-match — a calibrated verdict, not a made-up percentage.

Provenance on every value

Each number carries whether it's observed, computed, projected, or canon — so you always know how much to lean on it.

Gaps shown as gaps

When a figure isn't available at the grain you need, we say so — we don't fabricate a number to fill the cell.

Frozen, versioned editions

Pay and coordinate editions are pinned, so a comparison you make today still means the same thing next quarter.

Privacy by min-N

Any aggregate over people is suppressed below a minimum count, so no individual is identifiable.

Freshness

As of2026-07-30Editioncanon-v2@2.1.0-completeSourcecanon-v2 cell + salary-benchmark (model)