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Key statistics

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

5 of 8

Track

Management

Family rungs priced

M1 · M2 · M3 · M4 · M5 · M6

49-0000

manufacturing

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Directs coating and materials sourcing operations across multiple facilities to ensure equipment and parts meet protective and performance standards. At the Senior Director (M5) level, the role owns multiple functions or a large department, org-level trade-offs and investment, and drives multi-function results. Leads directors and managers.

What this level does

Direct the maintenance and reliability function across a division or multiple sites, with decisions that impact overall plant operations and production continuity
Define maintenance and reliability methods, standards, and KPIs organization-wide, resolving complex cross-site reliability and asset-lifecycle issues
Lead the maintenance organization through department managers, setting strategy for spare-parts strategy, CMMS/EAM platforms, and predictive-maintenance technology adoption
Influence executives and key equipment vendors on capital investment, equipment engineering design, and total-cost-of-ownership decisions
Own the division maintenance budget, reliability performance, and workforce strategy across the managed sites

Knowledge applied: Defines division/multi-site maintenance and reliability methods, standards, and asset-lifecycle strategy.
Complexity & problem solving: Solves complex org-wide reliability and asset-management issues with business-wide implications; defines the methods used.
Collaboration & interaction: Influences executives and major equipment vendors on capital and reliability decisions; leads through department managers.
Typical experience: 10–12+ years including second-level maintenance management and reliability strategy work.

Skills at this level

Preventive & Predictive Maintenance Program Management: — Plans and directs preventive, predictive, and condition-based maintenance schedules for production equipment, using vibration analysis, infrared thermography, ultrasonic testing, and oil analysis to anticipate and prevent equipment failures.
Equipment Troubleshooting & Repair Oversight: — Directs the diagnosis and repair of mechanical, electrical, hydraulic, and pneumatic faults on conveyor systems, pumps, motors, HVAC, and production machines, prioritizing interventions to minimize production downtime.
Multi-Disciplinary Technical Knowledge: — Applies solid working knowledge of mechanics, electricity, hydraulics, pneumatics, and control systems to evaluate maintenance work and guide technicians across all trades.
CMMS / Spare-Parts Inventory Management: — Manages maintenance work orders and spare-parts and indirect-materials inventory through a computerized maintenance management system, setting reorder points and driving backlog reduction and data accuracy.
System-Level Reliability Ownership: — Owns the reliability of equipment systems rather than individual machines — analyzing failure patterns across the asset population and directing interventions that improve overall uptime.
Cross-Functional Equipment Issue Resolution: — Coordinates with Facilities, Quality, and Manufacturing Engineering on problem identification, troubleshooting, and resolution of production equipment issues.
Equipment Engineering & Capital Evaluation: — Contributes to equipment engineering design evaluations and capital-replacement decisions, assessing aging or unreliable assets for repair-versus-replace and total-cost-of-ownership.
Team Coaching & Technician Development: — Trains and mentors junior technicians and line employees in maintenance procedures and safe operation of equipment, building qualified crews.
Predictive-Maintenance Systems & IoT Analytics: — Designs predictive/condition-based maintenance systems by analyzing datasets from IoT-instrumented equipment and applying machine learning to anticipate equipment failures.
Continuous Improvement / Lean Maintenance: — Applies Lean manufacturing and continuous-improvement techniques to maintenance workflows to reduce downtime, waste, and cost while sustaining quality.
Maintenance Safety & Compliance: — Enforces routine safety checks and safe-operation standards during maintenance work, maintaining clean work areas and compliance-oriented maintenance reporting.
Maintenance Strategy & Policy Formulation: — Sets maintenance and reliability strategy and formulates policies that standardize methods and drive innovation and technology integration across the maintenance organization.

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 M5 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$324k

San Francisco-Oakland-Fremont, CA$290k

Washington-Arlington-Alexandria, DC-VA-MD-WV$269k

Boulder, CO$265k

Seattle-Tacoma-Bellevue, WA$265k

Boston-Cambridge-Newton, MA-NH$254k

Lexington Park, MD$247k

Trenton-Princeton, NJ$246k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $194k; 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 M5 (M5 — Senior Director) from the levels around it — this level only. Every word is real canon for this profile.

Scope: Multiple functions or a large department
Autonomy: Owns multi-year strategy for the area
Complexity: Org-level trade-offs and investment
Impact: Multi-function results
Decision rights: Owns investment and org design across functions
Leadership: Leads directors and managers

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

Promotion

The next rung from M5 is M6.

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 Maintenance Officer	jf_maintenance::E5	$351,571	confident0.06
Expert Protective Coatings Specialist	jf_maintenance::P5	$142,824	confident0.06
Lead Protective Coatings Specialist	jf_maintenance::S5	$62,559	confident0.06
Senior Manager, Protective Coatings and Maintenance	jf_maintenance::M4	$173,021	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

M5

jf_maintenance::M5

Director of Maintenance

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: M
Superfunction: manufacturing
Function: manufacturing_expansion
Family: maintenance
Universal level: 5 / 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) · M5review0.17

Lean Manufacturing · M5review0.17

Machine Shop · M5review0.17

Manufacturing Engineering · M5review0.17

Pilot Plant Operations · M5review0.17

Production Test · M5review0.17

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)