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data quality monitoring jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-09-30, data quality monitoring appears in 356 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning data quality monitoring, with demand share up 5.2% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-30

Postings · last 90 days
356
Demand vs prior month
up 5.2% vs the prior 4 weeks
Top role · 20.2% of skill postings
Data Engineer
Top hiring metro
New York City

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Frequently asked questions about data quality monitoring

+Is data quality monitoring in demand in 2026?

Yes. data quality monitoring appears in 356 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Data Engineer accounts for the most postings mentioning data quality monitoring (20.2% of all postings mentioning data quality monitoring).

+What jobs require data quality monitoring?

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 data quality monitoring are Analytics Engineering Director (10.0% of that role’s postings mention data quality monitoring), Analytics Engineering Manager (8.3% of that role’s postings mention data quality monitoring), Data Product Owner (6.2% of that role’s postings mention data quality monitoring).

+What skills are commonly paired with data quality monitoring?

Across job postings indexed by Skillenai (90 days ending 2026-09-30), data quality monitoring most often appears alongside SQL, Python, data governance, data modeling, data pipelines.

+Where is data quality monitoring most in demand?

As of 2026-09-30, the metro areas posting the most jobs requiring data quality monitoring are New York City, London, San Francisco, Toronto, Boston, according to the Skillenai jobs index.

+How can I keep up with new data quality monitoring content and jobs?

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

+Which skills come before and after data quality monitoring?

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 data quality monitoring — last 90 days

Salary distribution

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

Career paths around data quality monitoring

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before data quality monitoring

Before data quality monitoringPower BI → data quality monitoring: 2 observed employer moves with this skill pairdata cleaning → data quality monitoring: 2 observed employer moves with this skill pairTableau → data quality monitoring: 2 observed employer moves with this skill pairinteractive reports → data quality monitoring: 1 observed employer moves with this skill pairdeployment time reduction → data quality monitoring: 1 observed employer moves with this skill pairMYSQL → data quality monitoring: 1 observed employer moves with this skill pairerror rates → data quality monitoring: 1 observed employer moves with this skill pairdata visualizations → data quality monitoring: 1 observed employer moves with this skill pairdata qualitymonitoringPower BI: 2 movesPower BI2 movesdata cleaning: 2 movesdata cleaning2 movesTableau: 2 movesTableau2 movesinteractive reports: 1 movesinteractivereports1 movesdeployment time reduction: 1 movesdeployment timereduction1 movesMYSQL: 1 movesMYSQL1 moveserror rates: 1 moveserror rates1 movesdata visualizations: 1 movesdatavisualizations1 moves

Skills after data quality monitoring

After data quality monitoringdata quality monitoring → sql: 3 observed employer moves with this skill pairdata quality monitoring → ETL: 2 observed employer moves with this skill pairdata quality monitoring → Power BI: 2 observed employer moves with this skill pairdata quality monitoring → R: 2 observed employer moves with this skill pairdata quality monitoring → queries: 1 observed employer moves with this skill pairdata quality monitoring → AWS Glue: 1 observed employer moves with this skill pairdata quality monitoring → real-time dashboard: 1 observed employer moves with this skill pairdata quality monitoring → time series analysis: 1 observed employer moves with this skill pairdata qualitymonitoringsql: 3 movessql3 movesETL: 2 movesETL2 movesPower BI: 2 movesPower BI2 movesR: 2 movesR2 movesqueries: 1 movesqueries1 movesAWS Glue: 1 movesAWS Glue1 movesreal-time dashboard: 1 movesreal-timedashboard1 movestime series analysis: 1 movestime seriesanalysis1 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: Power BI2
Before: data cleaning2
Before: Tableau2
Before: interactive reports1
Before: deployment time reduction1
Before: MYSQL1
Before: error rates1
Before: data visualizations1
After: sql3
After: ETL2
After: Power BI2
After: R2
After: queries1
After: AWS Glue1
After: real-time dashboard1
After: time series analysis1

Roles most likely to require data quality monitoring

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

RolePostings mentioning skill% of role postings mentioning skill
Analytics Engineering Director210.0%
Analytics Engineering Manager28.3%
Data Product Owner26.2%
Machine Learning Research Engineer25.9%
Data Analytics Lead25.3%
Platform Engineering Director15.0%
Product Data Analyst44.9%
Postdoctoral Researcher14.8%
Data Analytics Director14.2%
Data Consultant14.2%

Roles with the most data quality monitoring postings

RolePostings mentioning skillShare of skill postings
Data Engineer7220.2%
Data Analyst3911.0%
Analytics Engineer195.3%
Data Scientist164.5%
Software Engineer123.4%
Machine Learning Engineer72.0%
Product Manager61.7%
Data Platform Engineer51.4%
Product Engineer51.4%
Data Engineering Manager41.1%

Top companies posting jobs requiring data quality monitoring

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

Top companies posting jobs requiring data quality monitoring
CompanyPostings · 90 days
Wise7
City of New York6
Blend3605
Xanadu5
Ibotta5
Capital One4
Dropbox4
Jane Street3
Careers at Eucalyptus3
WPP3

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 data quality monitoring

NamePostingsShare
New York City174.8%
London154.2%
San Francisco123.4%
Toronto123.4%
Boston61.7%
Denver61.7%
Hyderabad61.7%
Mumbai61.7%
Madrid51.4%

Skills commonly paired with data quality monitoring

SQL (227)Python (173)data governance (123)data modeling (97)data pipelines (89)ETL (81)data lineage (69)DBT (68)data validation (68)Snowflake (64)

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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 data quality monitoring by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s data quality monitoring postings by all data quality monitoring 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
0af61c037ce74f48
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