Skillenai

Amazon Redshift jobs in 2026 — demand, top roles hiring, and related skills

As of 2026-11-04, Amazon Redshift appears in 603 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning Amazon Redshift, with demand share down 6.9% vs the prior 4 weeks.

Last updated · 90d ending 2026-11-04

Postings · last 90 days
603
Demand vs prior month
down 6.9% vs the prior 4 weeks
Top role · 31.2% of skill postings
Data Engineer
Top hiring metro
London

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Frequently asked questions about Amazon Redshift

+Is Amazon Redshift in demand in 2026?

Yes. Amazon Redshift appears in 603 job postings indexed by Skillenai over the 90 days ending 2026-11-04. Data Engineer accounts for the most postings mentioning Amazon Redshift (31.2% of all postings mentioning Amazon Redshift).

+What jobs require Amazon Redshift?

According to the Skillenai jobs index over the 90 days ending 2026-11-04, among roles with at least 20 postings, the highest shares mentioning Amazon Redshift are Lead Data Engineer (10.1% of that role’s postings mention Amazon Redshift), Analytics Associate (10.0% of that role’s postings mention Amazon Redshift), Economist (8.7% of that role’s postings mention Amazon Redshift).

+What skills are commonly paired with Amazon Redshift?

Across job postings indexed by Skillenai (90 days ending 2026-11-04), Amazon Redshift most often appears alongside Python, SQL, AWS, Snowflake, ETL.

+Where is Amazon Redshift most in demand?

As of 2026-11-04, the metro areas posting the most jobs requiring Amazon Redshift are London, New York City, Bengaluru, Hyderabad, San Francisco, according to the Skillenai jobs index.

+How can I keep up with new Amazon Redshift content and jobs?

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

+Which skills come before and after Amazon Redshift?

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 Amazon Redshift — last 90 days

Salary distribution

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

Career paths around Amazon Redshift

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before Amazon Redshift

Before Amazon Redshiftpython → Amazon Redshift: 44 observed employer moves with this skill pairsql → Amazon Redshift: 32 observed employer moves with this skill pairPySpark → Amazon Redshift: 22 observed employer moves with this skill pairPower BI → Amazon Redshift: 20 observed employer moves with this skill pairTableau → Amazon Redshift: 20 observed employer moves with this skill pairAzure Data Factory → Amazon Redshift: 16 observed employer moves with this skill pairspark → Amazon Redshift: 14 observed employer moves with this skill pairsnowflake → Amazon Redshift: 13 observed employer moves with this skill pairAmazonRedshiftpython: 44 movespython44 movessql: 32 movessql32 movesPySpark: 22 movesPySpark22 movesPower BI: 20 movesPower BI20 movesTableau: 20 movesTableau20 movesAzure Data Factory: 16 movesAzure Data Factory16 movesspark: 14 movesspark14 movessnowflake: 13 movessnowflake13 moves

Skills after Amazon Redshift

After Amazon RedshiftAmazon Redshift → snowflake: 21 observed employer moves with this skill pairAmazon Redshift → Azure Data Factory: 18 observed employer moves with this skill pairAmazon Redshift → sql: 17 observed employer moves with this skill pairAmazon Redshift → python: 16 observed employer moves with this skill pairAmazon Redshift → Power BI: 15 observed employer moves with this skill pairAmazon Redshift → Apache Kafka: 12 observed employer moves with this skill pairAmazon Redshift → Tableau: 10 observed employer moves with this skill pairAmazon Redshift → terraform: 9 observed employer moves with this skill pairAmazonRedshiftsnowflake: 21 movessnowflake21 movesAzure Data Factory: 18 movesAzure Data Factory18 movessql: 17 movessql17 movespython: 16 movespython16 movesPower BI: 15 movesPower BI15 movesApache Kafka: 12 movesApache Kafka12 movesTableau: 10 movesTableau10 movesterraform: 9 movesterraform9 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: python44
Before: sql32
Before: PySpark22
Before: Power BI20
Before: Tableau20
Before: Azure Data Factory16
Before: spark14
Before: snowflake13
After: snowflake21
After: Azure Data Factory18
After: sql17
After: python16
After: Power BI15
After: Apache Kafka12
After: Tableau10
After: terraform9

Roles most likely to require Amazon Redshift

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

RolePostings mentioning skill% of role postings mentioning skill
Lead Data Engineer710.1%
Analytics Associate210.0%
Economist28.7%
Cloud Data Engineer48.5%
Business Intelligence Engineer78.0%
Delivery Consultant36.7%
Business Intelligence Developer66.0%
Data Infrastructure Engineer25.9%
Database Architect25.4%
Information Technology Specialist15.0%

Roles with the most Amazon Redshift postings

RolePostings mentioning skillShare of skill postings
Data Engineer18831.2%
Software Engineer569.3%
Analytics Engineer223.6%
Data Architect223.6%
Data Analyst203.3%
Data Scientist193.2%
Solution Architect101.7%
Data Platform Engineer81.3%
Full Stack Engineer81.3%
Business Intelligence Engineer71.2%

Top companies posting jobs requiring Amazon Redshift

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

Top companies posting jobs requiring Amazon Redshift
CompanyPostings · 90 days
Capital One15
Mixpanel12
General Dynamics Information Technology9
Sensor Tower8
Cloudbedsthirdpartyboard8
Sportradar7
Elsevier7
Barclays6
Sigma6
Sixt6

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 Amazon Redshift

NamePostingsShare
London193.2%
New York City193.2%
Bengaluru172.8%
Hyderabad111.8%
San Francisco101.7%
Barcelona91.5%
Pune81.3%
Toronto81.3%
Lisbon71.2%

Skills commonly paired with Amazon Redshift

Python (420)SQL (399)AWS (226)Snowflake (216)ETL (198)AWS Glue (190)Amazon S3 (165)data modeling (140)AWS Lambda (139)DBT (136)

Explore related pages

How this was computed

Weekly job-count charts omit the incomplete current week (weeks start Monday, UTC). Totals include all postings in the stated window. Counts derive from the Skillenai jobs index over the 90 days ending 2026-11-04. 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 Amazon Redshift by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Amazon Redshift postings by all Amazon Redshift 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: 0.3% to 0.3%. 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 2,472 identified employers and covers 71% of earlier and 70% of latest indexed postings. Windows: 2026-07-07 to 2026-10-05 and 2026-08-04 to 2026-11-02 (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
3cd782f1a63c4fbf
data_as_of
2026-11-04
window_days
90
Compiled by Jared Rand · Data sourced from the Skillenai labor market index