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

As of 2026-09-30, Dataflow appears in 303 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning Dataflow, with demand share up 0.7% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-30

Postings · last 90 days
303
Demand vs prior month
up 0.7% vs the prior 4 weeks
Top role · 36.3% of skill postings
Top hiring metro
Bengaluru

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Frequently asked questions about Dataflow

+Is Dataflow in demand in 2026?

Yes. Dataflow appears in 303 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Data Engineer accounts for the most postings mentioning Dataflow (36.3% of all postings mentioning Dataflow).

+What jobs require Dataflow?

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 Dataflow are Data & Analytics Engineer (9.5% of that role’s postings mention Dataflow), Data Engineering Director (7.0% of that role’s postings mention Dataflow), Lead Data Engineer (5.3% of that role’s postings mention Dataflow).

+What skills are commonly paired with Dataflow?

Across job postings indexed by Skillenai (90 days ending 2026-09-30), Dataflow most often appears alongside BigQuery, Python, SQL, Pub/Sub, Cloud Storage.

+Where is Dataflow most in demand?

As of 2026-09-30, the metro areas posting the most jobs requiring Dataflow are Bengaluru, London, Austin, Hyderabad, Paris, according to the Skillenai jobs index.

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

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

+Which skills come before and after Dataflow?

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 Dataflow — last 90 days

Career paths around Dataflow

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before Dataflow

Before Dataflowpython → Dataflow: 30 observed employer moves with this skill pairPySpark → Dataflow: 22 observed employer moves with this skill pairHive → Dataflow: 17 observed employer moves with this skill pairsnowflake → Dataflow: 17 observed employer moves with this skill pairspark → Dataflow: 15 observed employer moves with this skill pairsql → Dataflow: 15 observed employer moves with this skill pairKafka → Dataflow: 14 observed employer moves with this skill pairRedshift → Dataflow: 13 observed employer moves with this skill pairDataflowpython: 30 movespython30 movesPySpark: 22 movesPySpark22 movesHive: 17 movesHive17 movessnowflake: 17 movessnowflake17 movesspark: 15 movesspark15 movessql: 15 movessql15 movesKafka: 14 movesKafka14 movesRedshift: 13 movesRedshift13 moves

Skills after Dataflow

After DataflowDataflow → Redshift: 19 observed employer moves with this skill pairDataflow → PySpark: 17 observed employer moves with this skill pairDataflow → python: 16 observed employer moves with this skill pairDataflow → AWS Glue: 16 observed employer moves with this skill pairDataflow → Kafka: 12 observed employer moves with this skill pairDataflow → snowflake: 12 observed employer moves with this skill pairDataflow → Power BI: 11 observed employer moves with this skill pairDataflow → S3: 10 observed employer moves with this skill pairDataflowRedshift: 19 movesRedshift19 movesPySpark: 17 movesPySpark17 movespython: 16 movespython16 movesAWS Glue: 16 movesAWS Glue16 movesKafka: 12 movesKafka12 movessnowflake: 12 movessnowflake12 movesPower BI: 11 movesPower BI11 movesS3: 10 movesS310 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: python30
Before: PySpark22
Before: Hive17
Before: snowflake17
Before: spark15
Before: sql15
Before: Kafka14
Before: Redshift13
After: Redshift19
After: PySpark17
After: python16
After: AWS Glue16
After: Kafka12
After: snowflake12
After: Power BI11
After: S310

Roles most likely to require Dataflow

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

RolePostings mentioning skill% of role postings mentioning skill
Data & Analytics Engineer29.5%
Data Engineering Director57.0%
Lead Data Engineer45.3%
Security Operations Engineer34.8%
Data Engineering Lead34.2%
Data Modeler14.0%
Senior Data Scientist13.7%
Technical Lead Manager13.4%
Business Intelligence Lead12.8%
AI Solution Architect22.6%

Roles with the most Dataflow postings

RolePostings mentioning skillShare of skill postings
Data Engineer11036.3%
Software Engineer258.3%
Cloud Engineer82.6%
Data Architect82.6%
Data Scientist72.3%
DevOps Engineer62.0%
AI Engineer51.7%
Backend Engineer51.7%
Data Engineering Director51.7%
Data Platform Engineer51.7%

Top companies posting jobs requiring Dataflow

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

Top companies posting jobs requiring Dataflow
CompanyPostings · 90 days
66degrees12
Capco9
Bloomreach8
Capital One6
Baytech Consulting6
Mattel6
Mozilla5
Spotify5
SFEIR5
PwC4

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 Dataflow

NamePostingsShare
Bengaluru185.9%
London93.0%
Austin82.6%
Hyderabad72.3%
Paris62.0%
Pune62.0%
Chicago51.7%
New York City51.7%
Toronto51.7%

Skills commonly paired with Dataflow

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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 Dataflow by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Dataflow postings by all Dataflow 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
a2675cd92a8dac97
data_as_of
2026-09-30
window_days
90
Hiring engineers who use Dataflow?

The demand, skills, and geo numbers on this page come from the same Skillenai labor market index that powers our API. Use it for compensation benchmarking, hiring-competition analysis, and skill-adoption tracking.

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Compiled by Jared Rand · Data sourced from the Skillenai labor market index