Data Engineer in IT — CIS and Europe market
Data Engineer — data engineering: ETL/ELT pipelines, DWH modelling, streaming processing, task orchestration, data lakes. The largest segment of the Data direction, at the boundary of backend development and analytics. Role family: Data Engineer (general — pipelines + DWH), ETL Developer (classical batch — Informatica/SSIS legacy + Airflow), Analytics Engineer (dbt + DWH, at the boundary with Analyst), Pipeline Engineer (focus on orchestration — Airflow/Dagster/Prefect), Data Platform Engineer (infra for data teams — Kubernetes + Spark cluster + Delta Lake/Iceberg). Stack: Python (must — pandas, PySpark, cloud SDKs), SQL (must, advanced — window functions, CTEs, optimisation), Airflow/Dagster/Prefect (orchestration), Spark/Flink/Kafka Streams (big data + streaming), dbt (analytics engineering — transform layer), DWH: Snowflake/BigQuery/ClickHouse/Redshift/Greenplum, Kafka+Debezium (CDC), S3/HDFS/Delta Lake/Apache Iceberg, AWS/GCP/Azure data stacks, Docker+Kubernetes. According to Zorky CRM, 2621 active openings with a median salary of $6250/mo. Top stack: python, sql, spark, databricks, go. 40.8% remote. Data Engineer pays 10-20% above Backend Senior in Moscow — premium for SQL + distributed systems.
Comparison with other specializations
The Data Engineering direction contains 5 specializations. The current one (Data Engineer) is highlighted in blue — compare it with its neighbors by the number of open jobs and median salary.
Demand trend
Data Engineer produces the largest and most stable flow of jobs among data segments. Demand grows especially at CIS product companies (legacy DWH migration to modern data stack — Snowflake/dbt/Airflow) and at FinTech.
How many new jobs appear each week.
Seniority distribution — trend
How the share of Junior/Middle/Senior/Lead in open jobs shifts week over week. A trend toward Senior usually signals a mature specialization where companies look for ready-made talent; the opposite — a rise in Junior — signals expansion and ground-up team building.
Share of each level in % of all jobs with a stated grade per week.
Salary by level
Data Engineer salary ladder: Junior $3759, Middle $5750, Senior $6720, Lead $9981 /mo. Junior rare — the market hires after Python Middle or Analyst Senior with advanced SQL. Career flow: Junior → Middle → Senior → Analytics Engineer / Data Platform / Head of Data.
Median salary (USD/month) at each grade plus the jump vs the previous one.
Biggest salary jump — between Junior and Middle (+92.1%).
Salary distribution — trend
The median Data Engineer salary — $6250/mo — one of the highest in the data segment. Most jobs sit at $4-9K. $12K+ — Senior with Spark/Flink + Snowflake/BigQuery at international SaaS.
What share of jobs each price band holds week over week.
50% of jobs are in the $5–8K range (the core market). High-end $8K+ segment: 22% — usually US-remote or senior-international roles.
Hiring geography
The leader by Data Engineer job count is 🇵🇱 Poland (548 positions). Moscow dominates thanks to the huge data teams at Yandex, Sber, Tinkoff. Poland — second by remote-job count for Russian-speakers.
Job distribution by country.
These numbers reflect the distribution across the sources we parse. Poland often looks dominant because of dense NoFluffJobs / JustJoin.it / Pracuj coverage — the Polish IT market is genuinely large, but in our sample its share is overweighted relative to the real volume of all IT jobs in the region. Same caveat for other top countries: this is «where our parsers look», not «the true size of the market».
Remote / Hybrid / Office — trend
40.8% of Data Engineer jobs are remote or hybrid. Work is fully cloud-based. Sber — more often office due to data residency / compliance. Tinkoff/Avito — hybrid or remote.
How the share of each work format shifts week over week.
Balanced market: 41% remote, 14% hybrid, 45% office.
Top in-demand technologies
Top Data Engineer stack 2026: Python + advanced SQL (must), Airflow / Dagster / Prefect (orchestration), Spark / Flink / Kafka Streams (big data + streaming), dbt (transform layer), Snowflake / BigQuery / ClickHouse / Redshift (DWH), Kafka + Debezium (CDC), Delta Lake / Iceberg (lakehouse), Docker + Kubernetes. Senior — observability + data quality.
Technology combinations
Common pairs: Python + SQL, Python + Airflow, Spark + Hadoop, dbt + Snowflake, Kafka + Spark Streaming, Airflow + BigQuery. Learning roadmap: Python + SQL → Airflow → one DWH → dbt → PySpark → Kafka basics.
Which pairs of technologies appear together most often in a single job.
Where we see these jobs
Data Engineer jobs: hh.ru, Habr Career, getmatch (large data segment), Polish boards (NoFluffJobs/JustJoin.it), Telegram (@data_engineering_jobs, @dataeng_search, @ODS Jobs), LinkedIn (international SaaS), internal HRMS of large product companies.
Data Engineer vs other directions
Data Engineer — the largest segment of the data direction (60-70% of data jobs). By median 10-20% above Backend Senior in Moscow thanks to specialty premium. Comparison — in the SiblingSubnichesChart above.
Volume of open jobs across IT directions.
Latest jobs
Latest open Data Engineer jobs — the most recent 10 positions with adequate description quality. The full list is in our CRM or via the "see all" link below.
What we can offer
If you work with Data Engineer jobs or you're in this role yourself — we can close a specific task. Pick a format, leave a contact — we reply within 24 hours.
Frequently asked questions
The most common questions about Data Engineer: pay, Spark vs Flink vs Kafka Streams, Airflow vs Dagster, differences from Backend and ML Engineer, remote, how to start (12-18 months after Python Middle), Senior skills. Answers recompute automatically.
How much does a Data Engineer earn in 2026?
The median Data Engineer salary across CIS and Europe is $6250/mo per Zorky CRM data (2621 active jobs). Data Engineer pays 10-20% above Backend Senior thanks to a smaller supply of specialists and distributed-systems complexity. Junior $3759/mo, Middle $5750/mo, Senior $6720/mo, Lead $9981/mo. Senior Data Engineer + Spark/Flink + distributed DWH — $7,000-11,000/mo. Senior Analytics Engineer (dbt + Snowflake) — $6,500-9,500. Data Platform Engineer (Kubernetes + Spark + k8s operators) — premium $8,000-13,000/mo.
What does a Data Engineer Junior, Middle, Senior, or Lead earn?
Data Engineer salary ladder (median USD/mo): Junior $3759/mo, Middle $5750/mo, Senior $6720/mo, Lead $9981/mo. Junior openings are scarce — the market expects Python Middle + advanced SQL or a switch from Backend/Analyst. The Junior → Middle jump — Airflow + one DWH + Spark basics. Senior owns an entire data domain (e.g. event pipeline). Lead Data Engineer / Head of Data Platform — runs 3-8 engineers, owns architecture decisions + DWH model + data governance. Career flow: Backend Middle/Analyst Senior → Data Engineer Middle → Senior → either Analytics Engineering (dbt focus), Platform Engineering (infra focus), or Head of Data.
How much do Data Engineers earn in Moscow, St Petersburg, remote?
Moscow Senior Data Engineer — $6,500-10,000/mo (Yandex — largest employer, Tinkoff, Sber, Alfa, Avito, Wildberries, OZON, VK, X5 Retail Tech, OCS, Lamoda). The highest salaries in FinTech and AdTech (real-time event processing). St Petersburg $5,500-8,500. Minsk/Kyiv $4,000-7,000. Poland €5,500-9,000 gross Senior. Germany €75-105K/yr Senior. 40.8% remote. International SaaS (Databricks/Snowflake/dbt Labs/Confluent) — $9,000-15,000+ Senior for Russian-speakers on remote with English — data engineering is one of the highest-paid specialties in international remote.
What stack does a Data Engineer most often need?
Top 5: python, sql, spark, databricks, go. Python — must (pandas, PySpark, boto3, GCP SDK). SQL — advanced must (window functions, CTEs, query optimisation, EXPLAIN ANALYZE). Airflow — industry orchestration standard; Dagster/Prefect growing in new projects. Spark (PySpark/Scala) — big data batch + streaming. Kafka — mandatory for Middle+ in event-driven. Flink — premium skill for true streaming. dbt — transform layer standard in the modern data stack. DWH: Snowflake (cloud premium), BigQuery (GCP), ClickHouse (OLAP, dominates in CIS), Redshift (AWS legacy). Storage: S3/HDFS, Delta Lake/Apache Iceberg (lakehouse). Docker+Kubernetes. Terraform/Pulumi for IaC. Senior — observability (Datadog/Grafana + Great Expectations / Soda for data quality).
Spark vs Flink vs Kafka Streams — what to pick in 2026?
Spark — industry standard batch + micro-batch streaming. PySpark the largest job market, Spark Scala — premium niche. Used by 80%+ DWH teams. Downside: micro-batch latency (seconds-minutes), not true realtime. Flink — true streaming engine with millisecond latency, exactly-once semantics out of the box. Growing segment: FinTech/AdTech/IoT/real-time ML inference. Fewer jobs, but Senior Flink Engineer premium (+15-25% over Spark). Kafka Streams (Java/Scala-only) — lightweight streaming library, not a separate cluster. Good for microservice-level stream processing, poor for heavy joins/state. Strategy: Spark first (market size), then Flink for diversification and premium pay. Kafka Streams — a niche add-on for JVM backend engineers.
Airflow vs Dagster vs Prefect — what to learn for orchestration?
Apache Airflow — industry standard, dominates 80%+ of jobs. DAGs in Python, huge operator ecosystem. Downsides: schedule-based, not event-driven natively, poor dynamic DAG generation. Dagster — modern challenger (Elementl/Astronomer-like). Asset-centric, the best DX, native testing, data lineage out of the box. Growing segment: product startups, modern data stack. Senior Dagster — premium +10-15%. Prefect — between Airflow and Dagster in philosophy. Good for hybrid workflows (batch + streaming). Fewer jobs than Airflow, but growing. Strategy: Airflow first (market necessity), then Dagster as a modern alternative. Cron + Python scripts — outdated approach, don't learn as primary.
Can Data Engineers work remotely?
Yes, 40.8% of Data Engineer jobs are full-remote or hybrid. Work is fully cloud-based (AWS/GCP/Azure DWH stacks). Startups and international SaaS — full-remote. Russian product companies (Yandex/Tinkoff/Avito) — hybrid (1-3 office days) or remote after probation. Sber — more often office due to compliance (data residency). Relocant hubs for Russian-speaking Data Engineers: Dubai, Cyprus, Lisbon, Berlin, Warsaw, Tbilisi. English opens Databricks/Snowflake/dbt partners with a premium +25-40% over salary.
How is Data Engineer different from Backend and ML Engineer?
Backend Engineer — REST/GraphQL API, business logic, transactional DBs (PostgreSQL/MySQL). Focus: serve product requests. OLTP systems. Data Engineer — ETL/ELT pipelines, DWH modelling, streaming, batch processing of terabytes. Focus: move and transform large volumes of data. OLAP systems. ML Engineer — deploy models to production (FastAPI + ONNX/TorchServe), feature store, MLOps, model monitoring. Focus: production-ready inference. Career switch: Backend Senior → Data Engineer Middle in 4-8 months (Airflow + dbt + one DWH). Data Engineer → ML Engineer in 6-12 months (PyTorch + MLOps stack). By pay: Data Engineer ≥ Backend Senior in Moscow; ML Engineer ≈ Data Engineer Senior on average.
Which companies actively hire Data Engineers?
At the top: Yandex, Sber, Tinkoff. Large CIS product companies: Yandex (huge Data Platform for all services), Avito, Tinkoff, Wildberries, OZON, VK, Mail.ru, X5 Retail Tech, MTS Big Data, Lamoda, Samokat. Banks + fintech: Sber (huge data fleet), Alfa, Raiffeisen, Tinkoff Insurance. Telco big data: MTS, Beeline, Megafon. International with CIS teams: JetBrains, EPAM Data Practice, Luxoft, Wrike, inDriver. Growing international data-tooling: Databricks, Snowflake, dbt Labs, Confluent (Kafka), Astronomer (Airflow). Y Combinator startups in data/ML after Series A — premium pay $9,000-15,000+.
Where to start in Data Engineering in 2026?
Roadmap: 1) Python to Middle — pandas, numpy, typing, testing. 2) SQL to advanced — window functions, CTEs, subqueries, optimisation (EXPLAIN ANALYZE), indexes. PostgreSQL + one OLAP (ClickHouse recommended). 3) Airflow — official tutorial, write 3-5 DAGs. 4) One DWH: BigQuery (free tier) or Snowflake trial. Master modelling (star/snowflake schema, SCD). 5) dbt — analytics engineering layer. dbt tutorial + pet project with jaffle-shop dataset. 6) PySpark basics — DataFrame API + one pet project with 1-10 GB of data. 7) Kafka basics — producer/consumer + one streaming pet. 8) End-to-end pet project: pipeline from API/files through Airflow + dbt into DWH, dashboard on Metabase/Superset, deployed in the cloud. Courses: Karpov.Courses "Data Engineer", OTUS "Data Engineer", Yandex.Practicum "Data Engineer", DataCamp Data Engineer Track. Books: "The Data Warehouse Toolkit" Kimball, "Fundamentals of Data Engineering" Reis. Time to Middle (Junior rare) — 12-18 months full-time after Python Middle experience.
How many Data Engineer jobs are open across CIS and Europe?
2621 active open Data Engineer positions in the Zorky CRM sample. Geography: 🇵🇱 Poland, 🇺🇸 USA, SG. Sources: hh.ru, Habr Career, getmatch, Djinni, LinkedIn (especially many international Data jobs), Telegram (@data_engineering_jobs, @dataeng_search, @ODS Jobs), NoFluffJobs/JustJoin.it (Poland — big demand for Data Engineer remote for Russian-speakers), career pages of EPAM/Luxoft/Andersen Data Practice. The real market is broader than the sample thanks to internal-only positions at large banks and CIS product companies. Time to close a Senior Data Engineer role — 4-8 weeks.
What skills does a Senior Data Engineer need?
A Senior Data Engineer owns the full data-pipeline cycle. SQL: mastery (window functions, recursive CTEs, query optimisation, partition pruning, materialized views). Python: pandas/PySpark mastery, memory profiling, async for I/O-heavy ingestion. Distributed systems: Spark execution model (shuffle, broadcast, partitioning), Kafka exactly-once, two-phase commit, idempotency. DWH modelling: Kimball (star schema, SCD), Data Vault, lakehouse architecture (Delta Lake/Iceberg). Orchestration: Airflow DAG design (idempotent tasks, backfills, sensors), dynamic DAG generation. Streaming: Kafka design (partitions, consumer groups, exactly-once), Spark Streaming or Flink. Cloud: one of AWS/GCP/Azure at the architect level (IAM + cost optimisation + networking + storage tiers). Data quality: Great Expectations / Soda, monitoring metrics + alerts. DevOps: Docker, Kubernetes operators (Spark/Kafka on k8s), Terraform/Pulumi for IaC. Soft: code review, mentoring Middle, working with Analyst/PM on requirements. English — must for Senior+ (the data stack is predominantly EN).
Similar specializations
Methodology
- Data period: in the hero and copy — the last 3 months. In the charts — the full available observation period (since parsers were launched, usually 2-3 months).
- Data is collected automatically from 1000+ sources — Telegram channels and job boards across CIS and Europe.
- Only live open jobs with a clear description are counted. Spam and duplicates are filtered out.
- Salaries are converted to USD/month at the current rate. Outlier values (below $500 or above $50K) are filtered out.
- Levels are normalized: Mid → Middle, Intern/Trainee → Junior, Principal/Staff/Expert → Lead.
- The first 2 weeks of data (parser ramp-up period) are not shown in the charts.
- Data is recomputed every day.
Authorship and citation
Analytics prepared by Zorky Research Team. Last updated: July 22, 2026 at 5:25 PM UTC.
Data sources and methodology
Data is collected automatically from 1000+ sources — Telegram job channels and job boards across CIS and Eastern Europe (HH, Habr Career, Djinni, DOU, NoFluffJobs, JustJoin.it, Pracuj.pl and others). Parsing runs 24/7, duplicates are filtered by description and URL, salary outliers are stripped. Detailed methodology — on the "How it works" page.
Zorky CRM (2026). Data Engineer in IT: CIS and Europe market. Accessed: 7/22/2026. URL: https://zorky.tech/en/research/data