# Marketing Analytics Work in the United States, 2026: Evidence from 100 Current Vacancies

> A structured study of 100 current U.S. vacancies shows marketing analytics organized around governed measurement, funnel and cohort diagnosis, attribution limits, dashboards, and decision recommendations.

- Canonical page: https://mtfinstitute.com/insights/marketing-analytics-work-us-vacancy-evidence-2026/
- Content type: Article
- Editorial category: Research & Reports
- Publisher: MTF Institute of Management, Technology and Finance
- Author: MTF Institute Research Team- Published: 2026-09-17
- Updated: 2026-09-17
- Language: English
- Topics: Career Development, Marketing Analytics, Data Analysis, Business Intelligence, Marketing Measurement

## Marketing Analytics Work in the United States, 2026: Evidence from 100 Current Vacancies

The complete open research archive, including the visually reviewed PDF and rights-safe vacancy index, is preserved at [Zenodo DOI 10.5281/zenodo.22811872](https://doi.org/10.5281/zenodo.22811872).

## US Vacancy Evidence for Professional Marketing Analytics Practice

**Research note.** Evidence cutoff: 17 September 2026. Geography: United States. Corpus: exactly 100 unique, current vacancy URLs from 88 employers. This document reports labour-market evidence only. It does not define a curriculum, lesson sequence, assessment plan, landing page, or commercial proposition.

## Executive finding

The 100-record corpus describes marketing analytics as an applied measurement-to-decision function. Employers do not primarily ask analysts to assemble charts after a campaign. They ask them to determine what should be measured, establish trustworthy definitions and tracking, diagnose movement through acquisition and retention journeys, assess the contribution of marketing activity without overstating causality, and translate evidence into decisions about targeting, product experience, channel investment, or operating priorities. Dashboard construction is common, but it is consistently framed as an interface for recurring decisions rather than as an endpoint.

The strongest recurring signal is decision use. 70 of 100 records contain retained evidence about recommendations, actionability, decisions, influence, executive communication, or translating insights. Dashboard, reporting, scorecard, visualisation, or review work appears in 67 records. Funnel, pipeline, conversion, or journey analysis appears in 40. Cohort, retention, lifecycle, churn, activation, or lifetime-value analysis appears in 31. Attribution, incrementality, causal inference, holdouts, marketing-mix modelling, multi-touch methods, or lift testing appears in 30. These counts are not claims about the whole labour market; they are exact record-level mention counts within the fixed evidence corpus.

The evidence also clarifies an important professional boundary. Employers expect an analyst to be able to work with attribution outputs, but mature postings rarely treat an attribution model as proof of incremental impact. Vacancies connect attribution with experimentation, holdouts, causal inference, or marketing-mix methods. This supports a practice standard in which every analytical recommendation states what the data can establish, what remains associational, and which test or additional data would reduce the uncertainty.

## Research question and scope

The research question was: what observable tasks, tools, work products, quality standards, and decision responsibilities are present in current US vacancies relevant to marketing analytics, marketing measurement, growth analytics, lifecycle analytics, performance marketing analytics, and adjacent go-to-market analytics?

The course context supplied six areas of interest: measurement planning, funnel metrics, cohort analysis, attribution limits, dashboards, and decision recommendations. The search was deliberately broader than those phrases. Employers use overlapping titles such as Marketing Analyst, Growth Analyst, Marketing Data Scientist, Marketing Analytics Manager, Lifecycle Analytics Lead, Media Analytics Analyst, Customer Analytics Manager, and GTM Analytics. A narrow title-only search would therefore understate the role family and bias the corpus toward a few large employers.

The target geography was the United States. Vacancy search requests included that geography and a public-search recency filter of the past 30 days. The final records carry employer-supplied location labels. 0 records explicitly include “remote” in that label; this should not be read as the total number offering some remote work, because hybrid arrangements may appear only in the full vacancy text. Posted dates in the fixed corpus range from 2026-09-09 to 2026-09-17.

## Method

Collection used LinkedIn’s public, unauthenticated guest job-search and job-detail interfaces for the base corpus. After record-level QA identified one lexical false positive, the replacement vacancy was independently verified as currently listed through Strava’s public Ashby job-board API. No account, login, application form, or non-public employer system was accessed. Twelve base-search expressions were used: marketing analytics; growth analytics; marketing data analyst; marketing measurement; customer analytics marketing; lifecycle analytics; performance marketing analyst; digital marketing analyst; media analytics; marketing science; GTM analytics; and acquisition analytics. Search requests specified United States and the past-30-days window. Results were deduplicated by source-platform job identifier and public vacancy URL.

Title relevance was determined before detailed analysis. A title had to contain both a domain signal—such as marketing, growth, acquisition, lifecycle, customer, media, campaign, GTM, revenue, digital, or ecommerce—and an analytical signal—such as analyst, analytics, data, measurement, insights, intelligence, science, operations, optimisation, or performance. This rule retained adjacent roles when their stated work was materially relevant while excluding generic marketers, sales roles, and unrelated data positions.

The automated base discovery pass identified 413 unique relevant posting identifiers. A bounded detail pass was attempted for a recency-sorted set of 160. The public endpoint returned usable short evidence for 102 and rate-limited 58 requests. Independent QA then excluded LinkedIn job 4466431512, Washington Nationals “Senior Analyst, Player Projections - Amateur Acquisitions,” because its work concerned baseball player projections rather than marketing analytics. A current Strava “Lead Marketing Analyst” vacancy was verified through the public Ashby job-board API and added as the replacement. Eligible records were scored against predeclared dictionaries for the ten competency families and fifteen tool families reported below, and exactly 100 unique records were retained. Existing corpus identifiers were preserved, with the replacement assigned VAC-062. This fixed-size rule prevents later interpretation from silently changing the denominator. Because the corpus is exactly 100, each record count is numerically identical to its percentage.

For every retained record, the registry preserves a stable corpus identifier, public vacancy URL, public detail-evidence URL, employer, role, employer-supplied location, posted date, access timestamp, search terms that surfaced the record, and up to four short evidentiary excerpts. The analysis counts whether at least one retained field or excerpt matches a documented pattern family. It does not count repeated mentions within one vacancy. Therefore an employer that repeats “dashboard” ten times contributes one dashboard record, not ten observations.

## Quantitative findings

### Recurring competency families

| Evidence family | Records | Share of corpus |
|---|---:|---:|
| Decision Recommendations And Storytelling | 70 | 70% |
| Dashboard Reporting And Visualization | 67 | 67% |
| Funnel Pipeline And Conversion Analysis | 40 | 40% |
| Roi Budget And Economic Outcomes | 37 | 37% |
| Measurement Planning And Kpi Governance | 36 | 36% |
| Cohort Retention And Lifetime Value | 31 | 31% |
| Attribution Incrementality And Causal Limits | 30 | 30% |
| Experimentation And Test Design | 22 | 22% |
| Data Quality Tracking And Governance | 14 | 14% |
| Segmentation And Audience Analysis | 14 | 14% |

Three relationships matter more than the rank order alone. First, decision communication and dashboard work co-occur with core analytical tasks. The profession is not divided neatly into “technical analysts” and “business storytellers”; current roles routinely ask the same person to define a KPI, query or validate the data, produce a view, explain variance, and recommend an action. Second, economic outcomes are prominent: 37 records mention ROI, ROAS, CAC, spend, budget, investment, revenue, profit, or financial performance. Marketing analytics is therefore judged partly by its ability to connect behavioural signals to resource-allocation consequences. Third, tracking and governance are not peripheral implementation details. 14 records mention data quality, hygiene, governance, tracking, QA, audits, source-of-truth practices, documentation, or integrity. A mathematically correct analysis built on unstable event definitions does not meet the evident workplace standard.

### Work products

| Observable work product | Records | Share of corpus |
|---|---:|---:|
| Recurring Reports And Business Reviews | 55 | 55% |
| Recommendation Presentations Or Memos | 41 | 41% |
| Funnel Cohort Or Segment Analyses | 40 | 40% |
| Dashboards And Scorecards | 38 | 38% |
| Attribution Or Incrementality Models | 29 | 29% |
| Experiment Designs And Readouts | 22 | 22% |
| Forecasts And Budget Models | 11 | 11% |
| Measurement Frameworks And Metric Definitions | 9 | 9% |

The artifact evidence reinforces the workflow interpretation. Dashboards and scorecards appear as durable operating products; recurring reports, reviews, and readouts establish a cadence; frameworks and metric definitions establish consistency; experiment or holdout artifacts establish a testable comparison; and recommendation presentations or memos connect analysis to action. Funnel, cohort, segment, retention, and lifecycle analyses are often intermediate diagnostic artifacts rather than isolated deliverables. Forecasts and budget models extend the same logic toward planning.

### Tools visible in retained evidence

| Tool or environment | Records | Share of corpus |
|---|---:|---:|
| SQL | 31 | 31% |
| Tableau | 21 | 21% |
| Python | 20 | 20% |
| R | 18 | 18% |
| Power BI | 15 | 15% |
| Excel | 14 | 14% |
| Looker | 10 | 10% |
| Google Analytics / GA4 | 9 | 9% |
| Salesforce / CRM Analytics | 7 | 7% |
| Snowflake | 4 | 4% |
| Adobe Analytics | 2 | 2% |
| Amplitude | 2 | 2% |
| dbt | 2 | 2% |
| BigQuery | 1 | 1% |
| Mode / Hex | 0 | 0% |

These tool counts are intentionally conservative. They reflect only the short excerpts retained for audit, not every technology listed in each full vacancy. SQL is nevertheless the most visible technical substrate. Tableau, Looker, Power BI, spreadsheets, and employer-specific BI environments appear as alternative presentation and exploration layers. Python and R are visible where modelling, automation, or deeper statistical work is expected. The practical inference is tool plurality: employers value reproducible logic and decision-quality outputs across several stacks, so a professional standard should avoid equating competence with one vendor interface.

### Seniority and responsibility

The corpus contains 13 director-or-executive records, 11 manager records, 42 senior individual-contributor or lead records, 33 analyst or specialist records, and 1 record outside those title heuristics. This mixture is useful because it reveals a shared operating core across levels. More senior roles add portfolio prioritisation, budget influence, framework ownership, governance, and executive communication. Analyst-level roles more often emphasise query work, recurring reporting, campaign or segment analysis, dashboard maintenance, and readout preparation. The evidence does not support treating “recommendations” as a leadership-only responsibility: recommendation language is present across levels.

## Interpretation by focus area

### Measurement planning

Measurement planning is expressed through KPI definition, frameworks, tracking plans, instrumentation, documentation, governance, and standards. Employers want analysts to translate a business question into an observable outcome and maintain consistency across channels or teams. The evidence suggests that a credible plan needs at least a decision question, a primary outcome, diagnostic measures, event or field definitions, a population and time window, data ownership, validation checks, a reporting cadence, and a decision rule. It should also identify what cannot be measured with the available signals. This is a stronger standard than assembling a list of familiar marketing metrics.

The vacancy language also indicates that the plan is negotiated. Cross-functional partnership appears in 28 records through terms such as partner, stakeholder, cross-functional, or thought partner. Marketing, product, finance, sales, operations, analytics engineering, and data teams often share responsibility for the final measurement system. The analyst is expected to specify analytical requirements clearly, challenge ambiguous definitions, and make trade-offs visible.

### Funnel metrics

Funnel work spans lead, acquisition, activation, conversion, pipeline, customer journey, and revenue stages. The postings imply that funnel analysis is not merely conversion-rate arithmetic. Analysts must establish stage-entry rules, distinguish eligible populations from all traffic, choose appropriate observation windows, reconcile online and offline transitions, and surface where data loss may resemble user loss. B2B vacancies add pipeline, bookings, and sales-stage alignment; consumer roles more often emphasise activation, checkout, subscription, engagement, or repeat behaviour.

The decision value comes from diagnosis. A falling top-line conversion rate can reflect a different traffic mix, a tracking break, a longer lag, a channel shift, or genuine behavioural deterioration. The corpus repeatedly connects funnel monitoring with segmentation, experiments, forecasts, and recommendations. That combination argues for an evidence standard in which the analyst decomposes a movement before prescribing a response.

### Cohort, lifecycle, and value analysis

Cohort-related evidence includes retention, churn, activation, lifecycle, repeat use, segmentation, customer value, and LTV. Employers use cohorts to separate composition effects from behavioural change and to compare customers acquired under different campaigns, channels, products, or time periods. The most decision-relevant postings connect cohort patterns to lifecycle interventions, audience prioritisation, product experience, or investment allocation.

The analytical risk is window inconsistency. Day-7 retention, monthly renewal, repeat purchase within 90 days, and predicted lifetime value answer different questions. A cohort result needs a declared anchor event, eligibility rule, age or maturity requirement, calendar treatment, and comparison baseline. In practical work, immature cohorts should not be compared directly with fully observed cohorts without censoring or a forecast assumption. The vacancies rarely spell out this statistical detail, but their expectations for rigorous retention and LTV decisions make it necessary.

### Attribution and its limits

Attribution evidence is strongest when combined with causal language. Vacancies refer to multi-touch models, internal attribution, marketing mix modelling, incrementality, lift, holdouts, experimental design, and causal inference. This is a material signal: employers recognise that channel-credit rules and causal contribution are not identical.

Attribution can organise observed touchpoints and support consistent reporting, but it is constrained by missing exposures, identity resolution, cross-device journeys, channel self-selection, platform reporting differences, time lags, privacy restrictions, and the model’s allocation rule. A last-touch, first-touch, linear, or data-driven model can produce useful descriptive views while still failing to answer what would have happened without the marketing exposure. The appropriate professional response is not to reject attribution; it is to label its purpose and limitations, compare it with experiments or quasi-experimental evidence where possible, and recommend a next measurement step proportionate to the decision’s cost.

### Dashboards as decision interfaces

Dashboard evidence includes self-service reporting, scorecards, operating reviews, executive views, monitoring, visualisation, and automated recurring deliverables. The dominant expectation is durability and use. A dashboard should preserve definitions, expose relevant segmentation and time context, make data freshness visible, and allow a stakeholder to detect a meaningful deviation. The most valuable designs connect a metric to an owner and to a next question or action.

This also defines what a dashboard is not. It is not a catalogue of every available metric, a substitute for analytical explanation, or proof that a channel caused an outcome. Dense visual production without decision context can increase rather than reduce ambiguity. The corpus supports treating narrative and recommendation quality as part of dashboard quality, especially for operating reviews and resource-allocation meetings.

### Decision recommendations

Recommendation language is the clearest unifying signal in the sample. The analyst is expected to move from evidence to a bounded action while preserving uncertainty. A decision-ready recommendation should distinguish observation from interpretation, connect the finding to a business objective, state the plausible alternatives, make assumptions explicit, identify risks and reversibility, and propose the next check or test. Senior roles add prioritisation across opportunities and budget consequences, but the structure is equally relevant to analyst-level readouts.

The evidence also supports calibrated language. “Increase spend because attributed ROAS is high” is weaker than a recommendation that explains the attribution method, notes selection and lag risks, triangulates with an incrementality test or cohort quality check, and defines a controlled scale-up with a stopping threshold. Decision quality comes from the chain of reasoning, not from certainty theatre.

## Role boundaries

The corpus suggests four boundaries. First, marketing analytics is a cross-functional partner, not an isolated reporting service: 28 records include partnership or stakeholder language. Second, it is decision support, not metric production only: 72 records contain recommendation, decision, actionability, influence, or strategy evidence. Third, the analyst is a measurement owner but not an unqualified causal claimant: 36 records include attribution, experimentation, incrementality, holdouts, or causal methods. Fourth, the analyst frequently specifies, validates, or consumes pipelines and tracking rather than replacing a data engineer: 11 records mention data-team collaboration, pipeline requirements, instrumentation, tracking, or source-of-truth practices.

These are not rigid organisational rules. Some postings combine analytics engineering, data science, experimentation, and management. The useful conclusion is that a marketing analyst must be technically fluent enough to inspect and challenge the measurement chain, while remaining accountable for business interpretation and recommendations. Where model development, identity stitching, or production pipeline ownership requires specialist engineering, the analyst should be able to define requirements and validate outputs without pretending to own every implementation layer.

## Evidence-led design implications

The findings support six design implications for later course-development work, without prescribing a curriculum here. First, any authentic performance task should begin with a governed measurement plan rather than with a dashboard canvas. Second, funnel and cohort analyses should be reproducible from explicit definitions, denominators, windows, and segment rules. Third, attribution work should require a written causal-limit statement and a proposal for triangulation through experiments, holdouts, quasi-experiments, or marketing-mix evidence. Fourth, dashboard evaluation should include decision ownership, cadence, thresholds, data freshness, and next-action logic. Fifth, a recommendation should be assessed as a professional artifact that separates evidence, assumptions, options, risks, and the next measurement action. Sixth, technical work should use SQL-shaped reasoning and tool-neutral schemas so that competence transfers across BI products.

The evidence does not justify turning the role into a catalogue of formulas or software demonstrations. It instead favours an integrated workflow: frame the decision; define and validate measurement; diagnose funnel and cohort behaviour; select an attribution or causal method appropriate to the question; communicate the result through an economical dashboard or readout; and make a bounded recommendation. That workflow is the labour-market through-line across industries and seniority levels.

## Limitations

This is a purposive, search-ranked corpus, not a probability sample of every US marketing analytics vacancy. Search visibility depends on platform indexing, employer syndication, query wording, and the collection time. The public detail endpoint rate-limited some requests, so the fixed corpus intentionally includes only records with retrievable short evidence and then ranks them by evidence coverage. This improves auditability but may overrepresent richly written vacancies and larger employers.

The count method is conservative and excerpt-bound. A missing mention means only that the concept was not present in the retained metadata or short excerpts; it does not show that the employer rejects or ignores it. Tool counts are especially likely to be underestimates because snippets were selected for relevance to the six focus areas, not for exhaustive technology extraction. Seniority classification is a title heuristic, and employer-provided location labels were not normalised into a remote/hybrid/on-site taxonomy.

Vacancies are dynamic. Employers may revise or withdraw them after the access timestamp. Two postings from the same employer can represent distinct vacancies, locations, or teams; uniqueness here is based on the public job-posting identifier and URL, not on title text. The evidence should therefore be treated as a time-stamped market snapshot and refreshed before any later claim of current demand.

## Rights and reproducibility note

The evidence registry stores factual metadata, public URLs, access timestamps, search provenance, and short excerpts needed to audit the coding. It does not reproduce full vacancy descriptions or application content. All excerpts remain attributable to their source URLs and are used for research synthesis. No authentication, application submission, personal data entry, or interaction with employer systems occurred.

Reproduction requires rerunning the twelve documented queries against the public LinkedIn guest search with United States and the past-30-days filter, applying the recorded title and subject-matter rules, and verifying the corrective Strava record through its public Ashby job-board endpoint. Records are deduplicated by source identifier and public URL, with short evidence retained from accessible detail pages. Because results and vacancy availability change, an exact future rerun should not be expected to return the same 100 URLs. The registry’s stable corpus IDs, job IDs, posted dates, access timestamp, source search URLs, and detail-evidence URLs preserve the audit trail for this cutoff. All percentages in this report use the fixed denominator of 100 and can be checked directly against `source-registry.json` and the documented pattern families in `vacancy-research-summary.json`.

## Conclusion

The current US vacancy evidence supports a coherent professional identity for marketing analytics: a practitioner who designs trustworthy measurement, explains movement through funnels and cohorts, uses attribution with explicit causal limits, builds decision-oriented reporting, and converts analysis into a recommendation that a stakeholder can act on. The role’s technical foundation is real, but technical output is judged by governance, interpretation, and decision consequence. This makes the measurement-to-decision chain—not any single dashboard or modelling technique—the most defensible centre of gravity for subsequent course design.



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