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

As of 2026-09-30, Parquet appears in 362 job postings indexed by Skillenai over the past 90 days — Data Engineer has the most postings mentioning Parquet, with demand share down 0.8% vs the prior 4 weeks.

Last updated · 90d ending 2026-09-30

Postings · last 90 days
362
Demand vs prior month
down 0.8% vs the prior 4 weeks
Top role · 34.0% of skill postings
Top hiring metro
San Francisco

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Frequently asked questions about Parquet

+Is Parquet in demand in 2026?

Yes. Parquet appears in 362 job postings indexed by Skillenai over the 90 days ending 2026-09-30. Data Engineer accounts for the most postings mentioning Parquet (34.0% of all postings mentioning Parquet).

+What jobs require Parquet?

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 Parquet are Data Platform Architect (8.1% of that role’s postings mention Parquet), Quality Test Engineer (7.1% of that role’s postings mention Parquet), Business Consultant (4.2% of that role’s postings mention Parquet).

+What skills are commonly paired with Parquet?

Across job postings indexed by Skillenai (90 days ending 2026-09-30), Parquet most often appears alongside Python, SQL, AWS, Spark, ETL.

+Where is Parquet most in demand?

As of 2026-09-30, the metro areas posting the most jobs requiring Parquet are San Francisco, New York City, Bengaluru, Pune, San Jose, according to the Skillenai jobs index.

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

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

+Which skills come before and after Parquet?

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 Parquet — last 90 days

Salary distribution

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

Career paths around Parquet

Skills documented before and after this skill across employer changes.

Historical career profiles · all locations

Skills before Parquet

Before Parquetpython → Parquet: 21 observed employer moves with this skill pairPySpark → Parquet: 9 observed employer moves with this skill pairAzure Data Factory → Parquet: 9 observed employer moves with this skill pairETL → Parquet: 8 observed employer moves with this skill pairsql → Parquet: 8 observed employer moves with this skill pairdatabricks → Parquet: 8 observed employer moves with this skill pairsnowflake → Parquet: 8 observed employer moves with this skill pairspark → Parquet: 7 observed employer moves with this skill pairParquetpython: 21 movespython21 movesPySpark: 9 movesPySpark9 movesAzure Data Factory: 9 movesAzure Data Factory9 movesETL: 8 movesETL8 movessql: 8 movessql8 movesdatabricks: 8 movesdatabricks8 movessnowflake: 8 movessnowflake8 movesspark: 7 movesspark7 moves

Skills after Parquet

After ParquetParquet → databricks: 12 observed employer moves with this skill pairParquet → PySpark: 8 observed employer moves with this skill pairParquet → snowflake: 8 observed employer moves with this skill pairParquet → ci/cd: 7 observed employer moves with this skill pairParquet → Power BI: 7 observed employer moves with this skill pairParquet → AWS: 6 observed employer moves with this skill pairParquet → Azure Data Factory: 6 observed employer moves with this skill pairParquet → airflow: 6 observed employer moves with this skill pairParquetdatabricks: 12 movesdatabricks12 movesPySpark: 8 movesPySpark8 movessnowflake: 8 movessnowflake8 movesci/cd: 7 movesci/cd7 movesPower BI: 7 movesPower BI7 movesAWS: 6 movesAWS6 movesAzure Data Factory: 6 movesAzure Data Factory6 movesairflow: 6 movesairflow6 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: python21
Before: PySpark9
Before: Azure Data Factory9
Before: ETL8
Before: sql8
Before: databricks8
Before: snowflake8
Before: spark7
After: databricks12
After: PySpark8
After: snowflake8
After: ci/cd7
After: Power BI7
After: AWS6
After: Azure Data Factory6
After: airflow6

Roles most likely to require Parquet

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

RolePostings mentioning skill% of role postings mentioning skill
Data Platform Architect38.1%
Quality Test Engineer27.1%
Business Consultant14.2%
Software Engineer, Backend14.2%
Technical Lead Manager13.4%
Data Engineering Lead22.8%
Senior Data Engineer12.8%
Lead Data Engineer22.7%
ML Ops Engineer12.4%
AI Systems Engineer12.2%

Roles with the most Parquet postings

RolePostings mentioning skillShare of skill postings
Data Engineer12334.0%
Software Engineer9927.3%
Data Scientist82.2%
ML Engineer82.2%
Data Analyst71.9%
Machine Learning Engineer61.7%
Engineering Manager51.4%
Graph Databases Director51.4%
Data Platform Engineer41.1%
Software Engineering Manager41.1%

Top companies posting jobs requiring Parquet

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

Top companies posting jobs requiring Parquet
CompanyPostings · 90 days
Siftstack13
Torcrobotics13
Barclays11
Anthropic6
Elsevier6
Cint6
Inetum6
Mastercard5
Dune5
Capco4

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 Parquet

NamePostingsShare
San Francisco277.5%
New York City143.9%
Bengaluru123.3%
Pune123.3%
San Jose123.3%
London92.5%
Toronto92.5%
Marina del Rey71.9%
Blacksburg51.4%

Skills commonly paired with Parquet

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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 Parquet by all postings for each role in the same window, ranking roles with at least 20 postings. Role distribution divides each role’s Parquet postings by all Parquet 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: 0.2% to 0.2%. 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
e8fb131fde56194c
data_as_of
2026-09-30
window_days
90
Hiring engineers who use Parquet?

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