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.

5 of 8

Track

Management

Family rungs priced

M1 · M2 · M3 · M4 · M5 · M6

17-0000

industrial_energy_operations

Coordinates

3 / 4 spaces

pay@2 · 2026-07

What this role is

Directs the surveying, mapping, and spatial-data function that supports oil and gas project planning and land management. 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

Directs the geomatics organization through department managers, impacting division-wide operations across surveying, GIS, remote sensing, and geospatial data services
Defines organization-wide methods and standards for spatial data acquisition, processing, database architecture, and mapping quality to resolve complex cross-project issues
Influences executives and major customers on key geospatial strategy decisions, technology investments, and multi-year program direction
Owns the division's operating and capital budget for geomatics staff, tools, and platforms
Sponsors adoption of new geospatial technologies (LiDAR, advanced remote sensing, automation) with business-wide implications

Knowledge applied: Defines organization-wide geospatial methods and standards impacting division-wide operations.
Complexity & problem solving: Complex org-wide issues in spatial data architecture, acquisition, and quality; defines methods.
Collaboration & interaction: Influences executives and major customers on key geospatial strategy and technology decisions.
Typical experience: 10–12+ years including second-level management and geomatics strategy work.

Skills at this level

Geomatics Team Leadership: — Directs field crews and office technical staff performing surveying, GIS, and geospatial data work, holding responsible charge for combined public and private sector land projects.
Geospatial Database Governance: — Sets standards for organizing, cleaning, and maintaining geospatial databases and large-scale geodatabases with multiple inputs to ensure data integrity and accuracy across projects.
Spatial Analysis Oversight: — Guides and reviews advanced geoprocessing and buffer-overlay analysis used to inform urban planning, environmental impact assessment, and transportation optimization decisions.
Mapping Deliverable QA: — Establishes and enforces quality-assurance processes for mapping products and deliverables before release to clients and stakeholders.
Geospatial Automation Strategy: — Directs the development of custom automation tools in Python or FME to streamline spatial data processing and mapping production at scale.
Remote Sensing & LiDAR Program Direction: — Sets direction for the acquisition and processing of satellite imagery, GNSS data, and LiDAR scanning as core inputs to the geomatics function.
Stakeholder Communication: — Explains geospatial assessments and presents complex spatial data in accessible reports and presentations to public- and private-sector stakeholders and executives.
Cross-Disciplinary Coordination: — Aligns geomatics teams with engineers, project managers, and data processors so spatial deliverables meet downstream engineering and planning needs.
Geomatics Project Management: — Designs, resources, and tracks complex mapping projects spanning field data collection and office analysis, managing scope, schedule, and budget.
GIS Platform Standards (ArcGIS/QGIS): — Defines organizational standards for spatial data processing and mapping on ArcGIS and QGIS to ensure consistent, competitive geomatics deliverables.
Geospatial Technology Strategy: — Sets multi-year investment and capability roadmaps for surveying, remote sensing, and geospatial data infrastructure with business-wide implications.
Talent & Capability Development: — Builds geoprocessing, database, and analysis competencies across the geomatics workforce and develops the leadership bench that sustains delivery.

Model-authored from 20 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

Geomatics — 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 17-0000 · national

Employment growth 2019–2024, BLS OEWSobserved

+17.4%Surveyors · United States

45,220 → 53,080 jobs

3,900 openings / year, split into:

replacement / exits 3,653

Growth — new jobs (6%)Exits — backfill (94%)

6% of hiring here is genuine expansion; the rest backfills people who move on.

Projected growth 2024–34 expanding: +4.4%
Growth share of hiring: 6%
Exit / retention pressure high: 94%
Openings / year: 3,900

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$306k

San Francisco-Oakland-Fremont, CA$274k

Washington-Arlington-Alexandria, DC-VA-MD-WV$254k

Boulder, CO$251k

Seattle-Tacoma-Bellevue, WA$251k

Boston-Cambridge-Newton, MA-NH$240k

Lexington Park, MD$234k

Trenton-Princeton, NJ$232k

Pay only — metro-level demand isn't in public data (it needs job postings). National median $184k; 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
Staff Geomatics Specialist	jf_geomatics::P5	$138,423†	confident0.06
Lead Geomatics Specialist	jf_geomatics::S5	$60,631†	confident0.06
Senior Manager, Geomatics	jf_geomatics::M4	$167,689†	confident0.06
Senior Director of Geomatics	jf_geomatics::M6	$241,631†	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 17-1022

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_geomatics::M5

Director of Geomatics

Geomatics

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: geoscience
Family: geomatics
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").

Geology · M5review0.17

Geophysics · M5review0.17

Geothermal · M5review0.17

Petrophysics · M5review0.17

Technologist · 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 17-1022	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)