Variance analysis jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-09-30, Variance analysis appears in 296 job postings indexed by Skillenai over the past 90 days — Program Manager has the most postings mentioning Variance analysis, with demand share up 14.8% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-30

Postings · last 90 days
296
Demand vs prior month
up 14.8% vs the prior 4 weeks
Top role · 8.8% of skill postings
Top hiring metro
San Francisco

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Frequently asked questions about Variance analysis

+Is Variance analysis in demand in 2026?

Yes. Variance analysis appears in 296 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Program Manager accounts for the most postings mentioning Variance analysis (8.8% of all postings mentioning Variance analysis).

+What jobs require Variance analysis?

According to the Skillenai jobs index over the 90 days ending 2026-09-30, among roles with at least 20 postings, the highest shares mentioning Variance analysis are FP&A Analyst (37.2% of that role’s postings mention Variance analysis), FP&A Manager (34.1% of that role’s postings mention Variance analysis), Finance Analyst (25.0% of that role’s postings mention Variance analysis).

+What skills are commonly paired with Variance analysis?

Across job postings indexed by Skillenai (90 days ending 2026-09-30), Variance analysis most often appears alongside forecasting, SQL, financial modeling, budgeting, Power BI.

+Where is Variance analysis most in demand?

As of 2026-09-30, the metro areas posting the most jobs requiring Variance analysis are San Francisco, New York City, London, Amsterdam, Boston, according to the Skillenai jobs index.

+How can I keep up with new Variance analysis content and jobs?

Skillenai indexes news, blog posts, and research papers mentioning Variance analysis alongside the jobs index. You can subscribe to a daily email digest of new Variance analysis content from your Skillenai account.

+Which skills come before and after Variance analysis?

The skill-flow chart shows skills documented in adjacent positions across observed employer changes. An outgoing skill is documented in the following position but not the preceding one. These are ideas to explore, not proven prerequisites, acquisition dates, or levels of mastery. Each ribbon counts employer moves with that skill pair; one move can contribute several pairs.

Weekly indexed postings requiring Variance analysis — last 90 days

Salary distribution

Box = 25th–75th percentile · tick = median · whisker = 10th–90th · USD, annualized

Career paths around Variance analysis

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before Variance analysis

Before Variance analysissql → Variance analysis: 4 observed employer moves with this skill pairTableau → Variance analysis: 4 observed employer moves with this skill pairpython → Variance analysis: 3 observed employer moves with this skill pairPower BI → Variance analysis: 2 observed employer moves with this skill pairreports → Variance analysis: 2 observed employer moves with this skill pairExcel → Variance analysis: 2 observed employer moves with this skill pairData Visualization → Variance analysis: 2 observed employer moves with this skill pairproduct-market fit analysis → Variance analysis: 1 observed employer moves with this skill pairVarianceanalysissql: 4 movessql4 movesTableau: 4 movesTableau4 movespython: 3 movespython3 movesPower BI: 2 movesPower BI2 movesreports: 2 movesreports2 movesExcel: 2 movesExcel2 movesData Visualization: 2 movesData Visualization2 movesproduct-market fit analysis: 1 movesproduct-market fitanalysis1 moves

Skills after Variance analysis

After Variance analysisVariance analysis → python: 4 observed employer moves with this skill pairVariance analysis → Data Extraction: 2 observed employer moves with this skill pairVariance analysis → data quality checks: 1 observed employer moves with this skill pairVariance analysis → Time Creation: 1 observed employer moves with this skill pairVariance analysis → Jupyter: 1 observed employer moves with this skill pairVariance analysis → Automation of manual workflows: 1 observed employer moves with this skill pairVariance analysis → quality targets: 1 observed employer moves with this skill pairVariance analysis → SAP outstanding issues/special projects: 1 observed employer moves with this skill pairVarianceanalysispython: 4 movespython4 movesData Extraction: 2 movesData Extraction2 movesdata quality checks: 1 movesdata qualitychecks1 movesTime Creation: 1 movesTime Creation1 movesJupyter: 1 movesJupyter1 movesAutomation of manual workflows: 1 movesAutomation ofmanual workflows1 movesquality targets: 1 movesquality targets1 movesSAP outstanding issues/special projects: 1 movesSAP outstandingissues/specialprojects1 moves
How to read this chart · view counts

Each side is an independent set of observed employer moves, not the same people followed through three stages. Ribbon widths compare move counts within that side. Internal moves are not included.

The following position documents a skill that the preceding position does not. Skills must be linked to both positions, with clear dates and no overlap. One move can connect several skill pairs. These patterns suggest skills to explore; they do not establish prerequisites, when a skill was learned, or a higher skill level.

Source: Skillenai talent graph, historical career profiles. Historical descriptions and coverage can change. Only the leading published connections are shown.

Observed connections and move counts
ConnectionMoves
Before: sql4
Before: Tableau4
Before: python3
Before: Power BI2
Before: reports2
Before: Excel2
Before: Data Visualization2
Before: product-market fit analysis1
After: python4
After: Data Extraction2
After: data quality checks1
After: Time Creation1
After: Jupyter1
After: Automation of manual workflows1
After: quality targets1
After: SAP outstanding issues/special projects1

Roles most likely to require Variance analysis

Among roles with at least 20 postings in the same period.

RolePostings mentioning skill% of role postings mentioning skill
FP&A Analyst1637.2%
FP&A Manager1434.1%
Finance Analyst825.0%
Finance Data Analyst27.4%
Marketing Analytics Director27.4%
Business Operations Analyst26.2%
Business Analytics Manager35.4%
Financial Data Analyst25.3%
Delivery Manager14.5%
Product Data Scientist23.2%

Roles with the most Variance analysis postings

RolePostings mentioning skillShare of skill postings
Program Manager268.8%
Business Analyst196.4%
FP&A Analyst165.4%
FP&A Manager144.7%
Data Analyst93.0%
Finance Analyst82.7%
Data Scientist62.0%
Strategic Finance Analyst62.0%
Financial Analyst51.7%
Systems Engineer51.7%

Top companies posting jobs requiring Variance analysis

Employers ranked by indexed job postings in the last 90 days.

Top companies posting jobs requiring Variance analysis
CompanyPostings · 90 days
Globalpr4
IonQ4
General Motors4
CLERA4
AstraZeneca4
Spacelift4
Google3
Advanced Micro Devices Inc.3
Crusoe3
Anthropic3

Job postings indexed over the past 90 days, grouped by resolved employer. Counts are postings, not hires. Companies without a published page appear without a link.

Top metros hiring for Variance analysis

NamePostingsShare
San Francisco103.4%
New York City93.0%
London62.0%
Amsterdam51.7%
Boston51.7%
San Jose51.7%
Pleasanton41.4%
Bengaluru31.0%
Chicago31.0%

Skills commonly paired with Variance analysis

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How this was computed

Counts derive from the Skillenai jobs index over the 90 days ending 2026-09-30. Skills are resolved against the Skillenai canonical taxonomy, so the same entity is counted whether a posting writes 'Python', 'Python 3', or 'python'. Role prevalence divides postings mentioning Variance analysis by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Variance analysis postings by all Variance analysis postings, including postings without a role. Shares need not sum to 100% for the displayed roles. Pages refresh weekly (or daily for the top-50 most-requested skills). Adjusted posting share: 0.1% to 0.1%. Demand share change is the relative percentage change between these adjusted shares. Each employer-and-ATS group has at least 10 postings in each 90-day window; its earlier posting count supplies the same weight in both windows. The panel includes 2,595 identified employers and covers 68% of earlier and 72% of latest indexed postings. Windows: 2026-06-02 to 2026-08-31 and 2026-06-30 to 2026-09-28 (UTC; end dates excluded). The windows overlap by 62 days. Dates reflect indexing, not the employer’s posting date. This measures posting mix, not total hiring or market-wide demand. Matching excludes entrants and exits; changes in crawl completeness within an employer or ATS can still affect the result.

source
Skillenai jobs index, deduplicated daily
entity_id
558f077691a4e856
data_as_of
2026-09-30
window_days
90
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Compiled by Jared Rand · Data sourced from the Skillenai labor market index