People Analytics Toolbox

Ask about this role

grounded in this role's JobFrame record + structure

Download this job's description (markdown) → derived live from this record.

Key statistics

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

4 of 8

Track

Executive

Family rungs priced

E1 · E2 · E3 · E4 · E5

11-0000

industrial_energy_operations

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Top executive accountable for all control-room, remote monitoring, and field operations across the enterprise's energy and mining assets. At the Senior Vice President (E4) level, the role owns multiple divisions, enterprise strategy and external stakeholders, and drives cross-division / enterprise. Leads division leaders.

What this level does

Oversee large portfolios spanning control operations and adjacent strategic areas (generation, storage, market participation) with high autonomy and corporate impact
Participate in enterprise strategic development — fleet growth, technology roadmap for remote monitoring and control, and market-expansion decisions across ISO regions
Engage the board, CEO and industry leaders regularly on operational reliability, systemic grid risk and the company's control-and-dispatch competitive position
Set cross-business strategy connecting real-time operations, regulatory strategy and commercial performance across the entire generation-and-storage portfolio
Hold ultimate accountability for the enterprise's operational safety, NERC compliance record and revenue-protection performance across all controlled facilities

Knowledge applied: Participates in enterprise strategic development, integrating control operations with generation, storage and market strategy across the full portfolio.
Complexity & problem solving: Solves corporate-impact problems with high autonomy — fleet technology roadmap, market expansion and systemic operational risk.
Collaboration & interaction: Engages the board, CEO and industry leaders regularly; sets cross-business strategy with broad organizational reach.
Typical experience: 20+ years of extensive corporate strategy and leadership experience across energy operations.

Skills at this level

SCADA and control systems proficiency: — Command of supervisory control software (SCADA) and distributed control systems (DCS) for real-time monitoring, operation and control of PV, BESS and hybrid facility equipment and outage management.
NERC and ISO regulatory compliance: — Ensures fleet operations conform to applicable local, state and federal regulations including NERC standards and ISO/ERCOT rules, governing outage reporting, switching coordination and market participation.
Grid and market operations: — Operates, monitors and dispatches distributed energy resources in accordance with market rules (e.g., ERCOT DRUC and market awards) to ensure continuous operation and revenue protection.
Outage coordination and revenue mitigation: — Identifies and resolves real-time and forced outages by coordinating with transmission system operators, scheduling coordinators and balancing authorities while mitigating lost revenue.
Emergency response leadership: — Directs readiness and execution of emergency shutdown, blow-down, fire and gas detection systems and standard emergency response roles across controlled facilities.
Fleet performance optimization: — Monitors plant performance metrics and sets strategies to maximize fleet output while minimizing downtime across day-to-day operations.
Operational P&L and cost control: — Owns P&L and cost control across multiple generation-and-storage locations, driving procurement, contracting and process optimization decisions.
Capital and operating recommendations: — Evaluates and recommends system improvements spanning capital and operating investments, cost control and process optimization for control-room infrastructure.
External regulatory and industry representation: — Represents the enterprise to regulators, ISOs, balancing authorities and industry bodies on grid reliability, market design and interconnection strategy.
High-pressure decision-making: — Applies strong analytical judgment to make quick, sound decisions during equipment or control problems and operational emergencies in the control room.

Model-authored from 19 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

Control Operations — 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 E4 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$632k

San Francisco-Oakland-Fremont, CA$567k

Washington-Arlington-Alexandria, DC-VA-MD-WV$525k

Boulder, CO$518k

Seattle-Tacoma-Bellevue, WA$518k

Boston-Cambridge-Newton, MA-NH$496k

Lexington Park, MD$483k

Trenton-Princeton, NJ$480k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $380k; 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 E4 (E4 — Senior Vice President) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Multiple divisions
Autonomy: Owns cross-division strategy
Complexity: Enterprise strategy and external stakeholders
Impact: Cross-division / enterprise
Decision rights: Owns strategy and investment across divisions
Leadership: Leads division leaders

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

Promotion

The next rung from E4 is E5.

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
Senior Manager, Control Operations	jf_control_operations::M4	$167,689†	confident0.06
Senior Control Room Operator	jf_control_operations::P4	$109,466†	confident0.06
Advanced Control Room Operator	jf_control_operations::S4	$56,408†	confident0.06
Chief Operations Officer	jf_control_operations::E3	$352,063†	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

E4

jf_control_operations::E4

Chief Operating Officer

Control Operations

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: E
Superfunction: industrial_energy_operations
Function: energy_utilities_business_operations
Family: control_operations
Universal level: 4 / 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").

Assets · E4review0.17

Emissions · E4review0.17

Energy Grid · E4review0.17

Energy Operations · E4review0.17

Land / Property · E4review0.17

Mining Operations · E4review0.17

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)