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

49-0000

manufacturing

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

The most senior executive accountable for the enterprise-wide maintenance, materials sourcing, and protective coatings strategy across all business units and regions. 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 the full manufacturing-operations portfolio in which maintenance/reliability sits — integrating asset reliability strategy with production, capital deployment, facilities, and safety across multiple strategic areas with high autonomy
Participate in enterprise strategic development, positioning equipment reliability and asset uptime as drivers of corporate operating performance and long-term capacity planning
Engage the board, CEO, and industry leaders regularly on major capital-asset investment, plant modernization, and enterprise reliability-risk exposure
Drive cross-business standardization of maintenance operating models, digital monitoring platforms (SCADA, PLC, DDC), and reliability governance across the corporation's manufacturing footprint
Sponsor enterprise reliability and Lean transformation programs, allocating capital and talent across regions to raise asset availability as a corporate operating priority

Knowledge applied: Participates in enterprise strategic development, integrating asset reliability across the broader manufacturing-operations portfolio (production, capital, facilities, safety, digital platforms) with high autonomy.
Complexity & problem solving: Solves corporate-impact problems spanning multiple strategic areas — plant modernization, network capacity, enterprise reliability-risk exposure — where decisions shape corporate operating performance.
Collaboration & interaction: Regularly engages the board, CEO, and industry leaders on major capital-asset and modernization decisions; broad cross-business/functional reach.
Typical experience: 20+ years of extensive corporate operations strategy and executive leadership experience.

Skills at this level

Equipment maintenance: — Directs preventive/routine and corrective maintenance of production equipment — conveyors, hydraulic lifts, HVAC, electrical and plumbing systems — to keep small technical faults from escalating into line-stopping failures.
Predictive maintenance techniques: — Deploys vibration analysis, infrared thermography, ultrasonic testing, and oil analysis to detect impending equipment failure and convert reactive downtime into scheduled intervention across the asset base.
Reliability-centered maintenance: — Establishes standardized maintenance programs, maintenance stores control, and Autonomous Maintenance so equipment reliability is engineered and governed rather than restored after breakdown.
Lean manufacturing: — Applies continuous-improvement techniques to maintenance and reliability work, removing waste from maintenance cycles and driving asset availability against uptime and cost targets.
Process improvement: — Redesigns maintenance and reliability workflows — from work-order flow to shift handoff and stores replenishment — to raise equipment performance and lower total maintenance cost.
Capital project execution: — Supports and prioritizes capital projects for plant equipment and facility modernization, weighing reliability gains, total cost of ownership, and production continuity in investment decisions.
SCADA and PLC monitoring: — Governs supervisory control and data acquisition (SCADA) and programmable logic controller (PLC) systems as the enterprise instrumentation layer for monitoring equipment condition and triggering reliability action.
Maintenance-management systems (SAP/ERP): — Uses SAP/ERP work-order, asset, and stores data to consolidate downtime, reliability, and maintenance-cost metrics into division- and enterprise-level performance reporting.
Energy management (DDC): — Applies digital direct-control (DDC) energy-management systems to facility maintenance so building and utility systems are run for reliability and energy efficiency across sites.
Maintenance reporting and KPI governance: — Consolidates routine maintenance reports and safety-check records into a KPI framework that tracks downtime, reliability, and cost as executive operating targets.
Team leadership under pressure: — Leads, guides, and inspires maintenance and reliability teams through equipment obstacles and high-pressure downtime situations, mentoring engineers and setting technical standards.

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

Maintenance — median with , by . Real canon data.

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 49-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

+23.7%First-Line Supervisors of Mechanics, Installers, and Repairers · United States

485,700 → 600,680 jobs

52,400 openings / year, split into:

replacement / exits 50,486

Growth — new jobs (4%)Exits — backfill (96%)

4% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +3.1%
Growth share of hiring: 4%
Exit / retention pressure high: 96%
Openings / year: 52,400

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$669k

San Francisco-Oakland-Fremont, CA$600k

Washington-Arlington-Alexandria, DC-VA-MD-WV$555k

Boulder, CO$548k

Seattle-Tacoma-Bellevue, WA$548k

Boston-Cambridge-Newton, MA-NH$524k

Lexington Park, MD$511k

Trenton-Princeton, NJ$508k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $402k; 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, Protective Coatings and Maintenance	jf_maintenance::M4	$173,021	confident0.06
Senior Protective Coatings Specialist	jf_maintenance::P4	$112,946	confident0.06
Protective Coatings Technician	jf_maintenance::S4	$58,202	confident0.06
Chief Maintenance Officer	jf_maintenance::E3	$363,257	confident0.06

Capability cluster

O*NET 49-1011

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_maintenance::E4

Group Chief Maintenance Officer

Maintenance

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: manufacturing
Function: manufacturing_expansion
Family: maintenance
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").

Chemistry, Manufacturing & Controls (CMC) · E4review0.17

Lean Manufacturing · E4review0.17

Machine Shop · E4review0.17

Manufacturing Engineering · E4review0.17

Pilot Plant Operations · E4review0.17

Production Test · 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 49-1011	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)