Data Engineering: market
A Data Engineer is an engineer who designs the infrastructure for collecting, storing, transforming, and delivering data: ETL and ELT pipelines, data warehouses, streaming, orchestration. The core stack is Python and SQL, orchestrators Apache Airflow or Dagster, distributed processing via Apache Spark, streams via Apache Kafka, warehouses ClickHouse, Snowflake, BigQuery, Redshift, transformations via dbt. The most in-demand technologies — sql, cloud, python, databricks, aws. 34% of positions are remote. Data refreshes daily from 101 job boards and 15 780 company career sites.
The Data Engineering market currently has 13,587 open roles, 12,779 of them freshly observed. Median salary $8,375/mo.
34% of Data Engineer jobs are full-remote; the rest are hybrid or office. Banks more often require hybrid because of compliance; startups and international teams — full-remote.
⚠ salary known for 834 of 13587 jobs; remote share from 11187 with a stated format; 12779 counted as fresh observations; trend and hiring difficulty are not shown
Demand and observed supply
| Open demand | 13,587 |
| Observed supply | — not published |
Over recent weeks the Data Engineer direction has produced a steady flow of new openings. Fluctuations are normal (postings cluster at the start of the month); watch the overall trend, not individual spikes.
⚠ candidates matching a vacancy are not counted yet: demand and the observed pool are shown
Salary distribution
One in ten earns under $4,019, one in ten over $16,004. Half the market falls between $5,751 and $12,500. Sample: 834.
Demand geography
| country | jobs |
|---|---|
| US | 3,849 |
| PL | 1,313 |
| GB | 1,080 |
| IN | 922 |
| DE | 487 |
| SG | 461 |
| CA | 409 |
| FR | 240 |
| RU | 217 |
| BR | 215 |
The leader by Data Engineer job count is US (3849 positions), followed by the major IT hubs of CIS and Eastern Europe. Important: this is the distribution across our parsing sources, not a global market estimate.
⚠ job counts only: salary by country is not published
Used together with
The top Data Engineer stack in 2026 is Python, SQL, Apache Spark, Apache Airflow, Apache Kafka. The Python + SQL baseline is required for everyone; specialisations follow: Spark (batch), Airflow (orchestration), Kafka (streaming).
The most common technology pairs in Data Engineer postings: Python+SQL, Airflow+Spark, Kafka+ClickHouse, dbt+Snowflake, Spark+S3. If you are planning a learning roadmap, these combinations maximise market coverage.
Demand by grade
| grade | jobs |
|---|---|
| senior | 4,134 |
| lead | 928 |
| principal | 705 |
| middle | 511 |
| junior | 320 |
The strongest pay growth is between Junior and Middle (picking up Airflow + Spark + a cloud platform).
⚠ demand side only: the grade of the observed pool is unknown for most of it
Employers
Demand is spread across 5,541 employers. The largest accounts for 2.3%, the top ten for 7.3%; the remaining 92.7% is long tail.
| share | value |
|---|---|
| top-1 | 2.3% |
| top-3 | 3.8% |
| top-10 | 7.3% |
| long tail | 92.7% |
The companies hiring Data Engineers most actively right now are large banks, marketplaces, search engines, and international data startups. Only those with 3+ open Data Engineer positions are shown.
⚠ names are not shown: staffing agencies and end employers are not yet told apart by the classifier
Where Zorky sees this market
Observed across 190 sources; the largest accounts for 19.1% — this market does not rest on a single channel.
Data Engineer jobs surface across most major sources: web parsers (HH, Habr Career, Djinni, DOU, NoFluffJobs, JustJoin.it) provide the bulk of the volume. Telegram channels add an exclusive stream — data startups, ClickHouse-specialised positions, US-remote offers.
Recent openings
- Finance SME – PD&IT (Downstream) · SA
- Développeur Data (Java,Power BI,SQL,GCP,Kafka,Terraform,GitHub),-(h/f) · FR
- SAP Analytics Cloud (SAC) Reporting, KPI & Dashboard Architect · SA
- GCP Cloud Engineer, Data Mesh and AI Platforms team · PL
- Junior Data Engineer · PH
- Data Engineer Intern / Junior (US Projects) · UZ
- Senior Data Engineer (Azure Databricks & DevOps, Remote, US Project) · UZ
- Product Engineer/Python-разработчик (Django, React, Claude Code / Codex) · RU
Latest open Data Engineer jobs — the most recent 10 positions with adequate description quality. The full list is available in our CRM or via the "see all" link below.
Adjacent markets
Data Engineer is one of the highest-paid IT specialisations by median. Click any direction's bar to compare salaries, stack, and dynamics in detail.
⚠ adjacent markets for comparison are not defined yet
How this is measured
- Vacancy
- an open job that cleared the quality gate and lists at least two technologies
- Observed candidate
- a candidate whose stack contains this technology; an aggregate — not a single record leaves the perimeter
- Matchable candidate
- not counted yet
- Window
- jobs open at the moment the snapshot was built
About the data
- Some breakdowns are hidden: their data coverage is not yet sufficient.
- Statistics are shown only where the sample clears a quality gate.
- A missing block does not mean a value of zero.
Breakdowns currently hidden: 8.
Data as of 2026-10-20
Specializations
Data Engineering breaks down into sub-specialisations: classic Data Engineer (ETL + warehouse), Big Data Engineer (Spark/Hadoop), Data Platform Engineer (infrastructure), DBA (database administration), Data Warehouse engineer (Snowflake/BigQuery), Streaming Engineer (Kafka). Each niche has its own salary range and toolset — click a card for detail.
Frequently asked questions
The most common questions about the Data Engineer market: salaries by level, stack (Spark vs Airflow vs Kafka), remote, comparison with Data Scientist and Analyst, where to start a career. Answers recompute automatically from current data.
What stack is most often required of a Data Engineer?
Top-5 technologies in Data Engineer postings across CIS and Europe: sql, cloud, python, databricks, aws. Required baseline skills: Python (advanced) and SQL (window functions, optimisation). The main orchestrator is Apache Airflow (the industry standard) or Dagster in newer teams. For distributed processing — Apache Spark (PySpark) or Flink. Streaming — Apache Kafka. Warehouses: ClickHouse (popular in RF), Snowflake, BigQuery, Redshift. Transformations — dbt (a growing trend). A cloud platform — one of AWS, GCP, or Azure is required at Senior. Docker, Kubernetes, Git, CI/CD are cross-cutting skills.
Who earns more — a Spark, Airflow, or Kafka specialist?
Most likely a specialist with experience across ALL three — people usually do not pick one. The most valuable Data Engineers on the market work with Spark (large-volume batch processing), Airflow (pipeline orchestration), Kafka (real-time streams). ClickHouse specialists are especially valued in RF teams.
Can Data Engineers work remotely?
Partially: 34% of Data Engineer jobs are full-remote. Less than Backend or Frontend, because banks and fintech often require hybrid or office due to compliance on work with PII and payment data — physical presence in a secured perimeter is needed. Marketplaces — more often hybrid. International product teams — almost always remote. Startups and agile teams — full-remote is the standard. Remote pay is often higher thanks to the international candidate pool. Few Junior remote openings exist — learning on live data is difficult without in-office mentorship.
How is a Data Engineer different from Data Scientist and Data Analyst?
A Data Engineer builds the infrastructure: ETL pipelines, warehouses, data streams. The goal is to make data collected, clean, and available at the right moment. Stack: Python, SQL, Spark, Airflow, Kafka. A Data Scientist analyses data and builds models: statistics, ML, A/B tests. Stack: Python, scikit-learn, PyTorch, Jupyter. A Data Analyst answers business questions through dashboards and reports. Stack: SQL, Excel, Tableau, Power BI, Looker. By pay: Data Engineer usually pays the most (most technical), Data Scientist close behind, Analyst sits 30–40% lower. Career flow: often Analyst → Data Engineer, or Analyst → Data Scientist via 1–2 years of reskilling.
How many Data Engineer jobs are open across CIS and Europe?
As of the latest data refresh, the Zorky CRM sample contains 16815 active open Data Engineer positions across CIS and Eastern Europe. These are postings published in the last 90 days — companies actually hiring. Geography is distributed; the leaders are US, PL, GB. Data is collected from 101 job boards and 15 780 company career sites: vacancy Telegram channels (an exclusive stream, especially for data startups), specialised job sites (HH, Habr Career, Djinni, DOU, NoFluffJobs, JustJoin.it, Pracuj.pl), and career pages of major fintech and marketplaces. Duplicates are filtered by description and URL. Seasonality: hiring usually peaks in February–March and September–October.
Where do Data Engineers earn more — in Russia or in Europe?
The main driver of the gap is contract currency and company type. Local Russian banks on rouble contracts have closed the gap to the Polish market for Senior over 2 years. Georgia attracts many remote relocants.
What skills does a Senior Data Engineer need?
A Senior Data Engineer owns the full data-infrastructure stack. Baseline: Python (advanced, async, optimisation), SQL (window functions, optimisation, plan reading), one cloud (AWS S3/EMR, GCP BigQuery/Dataflow, Azure Data Lake). Orchestration: Apache Airflow or Dagster (DAGs, sensors, custom operators). Distributed processing: Apache Spark (PySpark, optimisation, Catalyst). Streaming: Apache Kafka (producers, consumers, connectors). Warehouses: one of Snowflake, BigQuery, ClickHouse, Redshift. Transformations: dbt. Architectural patterns: lambda/kappa, data mesh, data lakehouse, slowly changing dimensions. Cross-cutting: Docker, Kubernetes, observability (Datadog, Prometheus), CI/CD.
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