Data Warehouse in IT — CIS and Europe market
Data Warehouse Engineer — specialisation in cloud DWH (Snowflake/BigQuery/Redshift/ClickHouse). A Data Engineer sub-segment with focus on DWH modelling + dbt + cloud data platforms. A premium niche thanks to cloud-DWH expertise. Role family: Snowflake Engineer (cloud-DWH dominator — schema design + dbt + cost optimisation), BigQuery Engineer (Google Cloud data stack), ClickHouse Engineer (CIS OLAP dominator — Yandex/Tinkoff/Avito), Redshift Engineer (AWS legacy), Data Warehouse Architect (modeling specialist — Kimball/Data Vault). Stack: SQL at mastery level (must — window functions, recursive CTE, query optimisation, partition pruning, materialised views), Snowflake (cloud premium — Tasks, Streams, Time Travel, micro-partitions, Snowpipe ingestion), BigQuery (GCP — slot-based pricing, BQML for in-DB ML), ClickHouse (OLAP dominator in CIS — ReplicatedMergeTree, distributed engines, materialised views), Redshift (AWS legacy — distkey/sortkey), Greenplum (CIS enterprise), dbt (transform layer standard + dbt-snowflake / dbt-bigquery / dbt-clickhouse adapters), Apache Airflow for ingestion orchestration, Looker/Mode/Metabase/Superset/Yandex DataLens for BI on DWH. According to Zorky CRM, 171 active openings with a median salary of $6756/mo. Top stack: snowflake, python, dbt, cloud, bigquery. 58.0% remote. Senior Snowflake/BigQuery — premium 10-15% over Senior Data Engineer for the cloud-DWH speciality.
Comparison with other specializations
The Data Engineering direction contains 5 specializations. The current one (Data Warehouse) is highlighted in blue — compare it with its neighbors by the number of open jobs and median salary.
Demand trend
Data Warehouse Engineer forms a steady job flow. Drivers: legacy DWH (Greenplum/Redshift) migrations to Snowflake/BigQuery/ClickHouse, modern data stack growth (dbt + cloud DWH), ClickHouse dominator in CIS product teams.
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
Juniors are scarce — market expects advanced SQL + one DWH. Career flow: Analyst Senior / Data Engineer Middle → DWH Middle → Senior → DWH Architect / Head of Data.
Median salary (USD/month) at each grade plus the jump vs the previous one.
Biggest salary jump — between Senior and Lead (+51.2%).
Salary distribution — trend
The median DWH salary — $6756/mo — premium 10-15% over Senior Data Engineer for the cloud-DWH speciality. Most jobs at $4-9K. $11K+ — Senior Snowflake at international Snowflake/dbt Labs shops.
What share of jobs each price band holds week over week.
49% 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 DWH job count is 🇵🇱 Poland (69 positions). Russia — ClickHouse dominator (Yandex/Tinkoff/Avito). Poland — DWH-friendly EU. Large international remote via Snowflake/dbt Labs.
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
58.0% of DWH jobs are remote or hybrid. Work is fully cloud-based. Russian product companies (Yandex/Tinkoff/Avito) — hybrid or remote. Sber — more often office. International cloud-DWH SaaS (Snowflake/BigQuery/dbt Labs) — full-remote.
How the share of each work format shifts week over week.
Balanced market: 40% remote, 13% hybrid, 47% office.
Top in-demand technologies
Top DWH stack 2026: SQL at mastery (must), one cloud DWH: Snowflake (cloud premium) / BigQuery (GCP) / ClickHouse (CIS OLAP dominator) / Redshift (AWS legacy), dbt (transform layer standard), Apache Airflow (ingestion orchestration), Apache Iceberg / Delta Lake (lakehouse), Kafka + Debezium (CDC), Looker / Mode / Metabase / Superset / Yandex DataLens (BI).
Technology combinations
Common pairs: SQL + Snowflake, SQL + ClickHouse, dbt + Snowflake, dbt + BigQuery, dbt + ClickHouse, Airflow + DWH. Learning roadmap: Advanced SQL → one cloud DWH (ClickHouse for CIS or Snowflake for international) → dbt → Kimball modeling → Airflow basics → one BI tool.
Which pairs of technologies appear together most often in a single job.
Where we see these jobs
DWH jobs: hh.ru, Habr Career, getmatch, Djinni, LinkedIn (huge international DWH segment — Snowflake/dbt Labs/Databricks), Telegram (@data_engineering_jobs, @dataeng_search, @ODS Jobs, @ClickHouse_ru), NoFluffJobs/JustJoin.it (Poland), career pages of EPAM Data Practice/Luxoft/Andersen Data, Snowflake/dbt partner networks.
Data Warehouse vs other directions
DWH Engineer — premium segment of the Data direction. Premium 10-15% over Senior Data Engineer for the cloud-DWH speciality. Comparison — in the SiblingSubnichesChart above.
Volume of open jobs across IT directions.
Latest jobs
Latest open DWH 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 Warehouse 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 DWH Engineer: pay, Snowflake vs BigQuery vs ClickHouse vs Redshift, dbt vs Airflow (complements, not competitors), differences from Data Engineer / Analytics Engineer, remote, how to start (6-10 months), Senior skills. Answers recompute automatically.
How much does a Data Warehouse Engineer earn in 2026?
The median Data Warehouse Engineer salary is $6756/mo per Zorky CRM data (171 active jobs). Junior —, Middle $6615/mo, Senior $7770/mo, Lead —. Senior Snowflake/BigQuery — premium 10-15% over Senior Data Engineer ($7,000-10,500/mo). Senior ClickHouse at Yandex/Tinkoff/Avito — $6,500-9,500. Data Warehouse Architect (modeling specialist) — premium $7,500-11,500. International remote via Snowflake/Databricks partners — $9,000-14,000 Senior.
What does a DWH Junior, Middle, Senior, or Lead earn?
DWH salary ladder (median USD/mo): Junior —, Middle $6615/mo, Senior $7770/mo, Lead —. Junior DWH openings are scarce — the market expects advanced SQL + one DWH hands-on (often after Analyst Senior with SQL mastery or Data Engineer Middle). The Junior → Middle jump — mastering one DWH deeply + dbt + ingestion pipelines. Senior owns DWH modelling architecture (star/snowflake/Data Vault). Lead — managing the modeling team + designing enterprise data architecture. Career flow: Analyst Senior / Data Engineer Middle → DWH Middle → Senior → either Data Warehouse Architect or Head of Data.
How much do DWH engineers earn in Moscow, St Petersburg, remote?
Moscow Senior DWH — $6,000-9,500/mo (Yandex Data Platform, Tinkoff, Avito, Sber, Wildberries, Ozon, X5 Tech, OCS, Lamoda — all use ClickHouse + dbt or Snowflake). St Petersburg $5,500-8,500. Minsk/Kyiv $4,500-7,500. Poland €5,500-9,000 gross Senior. Germany €75-105K/yr Senior. 58.0% remote. International cloud-DWH SaaS — Snowflake $10,000-15,000+ Senior, dbt Labs $9,500-14,000, Databricks $9,000-14,500 — all full-remote-friendly for Russian-speakers with English.
What stack does a DWH Engineer most often need?
Top 5: snowflake, python, dbt, cloud, bigquery. SQL at mastery (window functions, recursive CTE, query optimisation, partition pruning). One of cloud DWH: Snowflake (cloud premium — Tasks/Streams/Time Travel/micro-partitions/Snowpipe), BigQuery (GCP — slot pricing/BQML), ClickHouse (CIS OLAP dominator — ReplicatedMergeTree/distributed/materialised views), Redshift (AWS legacy — distkey/sortkey). Greenplum in CIS enterprise (Sber). dbt — must (transform layer standard — models + tests + macros + packages). Apache Airflow for ingestion. Apache Iceberg/Delta Lake — lakehouse table formats (growing). Apache Kafka + Debezium for CDC. BI: Looker/Mode/Metabase/Superset/Yandex DataLens. Knowledge of Kimball modeling (star/snowflake schemas, slowly changing dimensions) or Data Vault — Senior must.
Snowflake vs BigQuery vs ClickHouse vs Redshift — what to pick?
Snowflake — cloud-DWH dominator in international projects. Multi-cloud (AWS/Azure/GCP), separation of storage/compute, Time Travel, Snowpipe for streaming. Premium pricing, but best DX. Senior Snowflake — premium in international remote. BigQuery — Google Cloud-native. Slot-based pricing (or on-demand), BQML for in-DB machine learning. Dominator in GCP shops. ClickHouse — open-source OLAP dominator in CIS. Used by Yandex (creators), Tinkoff, Avito, Cloudflare, GitLab analytics. The fastest OLAP on columnar storage. Senior ClickHouse — market necessity at CIS product companies. Redshift — AWS legacy, migrations to Snowflake or BigQuery. Fewer new projects. Strategy: ClickHouse if at a CIS product team, Snowflake for international remote, BigQuery if in the GCP stack, Redshift only for maintenance of legacy AWS projects.
dbt vs Airflow — are they competitors?
Not competitors — complements. Airflow — orchestration (when to run what, dependencies between tasks). dbt — transform layer (SQL models + tests + documentation + lineage). Classic modern data stack pipeline: Airflow triggers ingestion (Python/Spark scripts) → dbt runs in Airflow as a BashOperator or DbtRunOperator (via the astronomer-dbt provider) → dbt executes SQL transformations in the DWH (Snowflake/BigQuery/ClickHouse) → BI layer (Looker/Metabase) reads the final dbt models. A Senior DWH Engineer owns both. Airflow for orchestration + ingestion, dbt for transformations + tests. Knowledge of dbt modules (sources/models/tests/macros/snapshots/seeds) + dbt best-practices — Senior must in 2026.
Can DWH engineers work remotely?
Yes, 58.0% of DWH jobs are remote or hybrid. Work is fully cloud-based (Snowflake/BigQuery/ClickHouse Cloud). Russian product companies (Yandex/Tinkoff/Avito) — hybrid or remote after probation. Sber — more often office due to data residency. International DWH SaaS — full-remote: Snowflake, BigQuery (Google), dbt Labs, Databricks. Relocant hubs: Berlin, Amsterdam, Zurich (data-friendly EU), Dubai, Cyprus, Lisbon. English — must for international remote with a premium +25-40%.
How is DWH Engineer different from Data Engineer / Analytics Engineer?
DWH Engineer (this page) — focus on cloud DWH (Snowflake/BigQuery/ClickHouse/Redshift) + modelling. SQL mastery + one DWH deeply. Data Engineer (general) — broader scope: pipelines + DWH + streaming + Spark. Analytics Engineer — focus on dbt + transform layer. Sits between Data Engineer and Data Analyst. SQL + dbt + one DWH (often Snowflake/BigQuery), but WITHOUT Spark and streaming. Pay: DWH Senior ≈ Data Engineer Senior, Analytics Engineer Senior 5-10% below (but the most accessible entry into a data career). Career switch DWH Engineer → Analytics Engineer — 1-2 months (same stack, focus on dbt). DWH Engineer → Data Engineer (general) — 3-6 months (+ Spark + Airflow deeper).
Which companies actively hire DWH Engineer?
At the top: Yandex, Avito, Tinkoff. Russian product companies with large data teams: Yandex Data Platform (huge ClickHouse + dbt stack), Tinkoff (ClickHouse + Greenplum), Avito (ClickHouse dominator), Sber (Greenplum + ClickHouse migration), Wildberries (ClickHouse + Snowflake), Ozon (ClickHouse), X5 Tech, OCS, Lamoda, Samokat. Banks: Alfa, Raiffeisen, VTB. EdTech: Skyeng, Uchi.ru, Skillbox. Outsourcing shops: EPAM Data Practice, Luxoft, Andersen Data. International cloud-DWH SaaS (full-remote premium for Russian-speaking Senior): Snowflake, dbt Labs, Databricks, BigQuery team (Google Cloud), ClickHouse Inc (international team on ClickHouse Cloud). Y Combinator startups with modern data stack — premium $9,000-14,000+.
Where to start in DWH in 2026?
Roadmap (SQL Middle or Data Analyst experience assumed): 1) Advanced SQL — window functions deeply, recursive CTE, query optimisation, EXPLAIN ANALYZE, indexes, partition pruning. PostgreSQL + one OLAP (ClickHouse recommended — most used in CIS). 2) One cloud DWH: ClickHouse (recommended for CIS career — open-source, free) OR Snowflake (for international remote — 30-day free trial) OR BigQuery (GCP free tier). Master engine-specific features (ClickHouse: ReplicatedMergeTree + distributed + materialised views; Snowflake: Tasks + Streams + Time Travel + micro-partitions). 3) dbt — official tutorial getdbt.com + Jaffle Shop pet project. Master models + tests + macros + sources + snapshots. 4) Kimball modeling — star schema, snowflake schema, slowly changing dimensions (SCD). Book "The Data Warehouse Toolkit" Kimball. 5) Airflow basics — ingestion orchestration. 6) Apache Iceberg or Delta Lake — lakehouse basics. 7) One BI tool — Metabase/Superset/Yandex DataLens (free) or Looker (premium). 8) End-to-end pet project: ingestion (Python script + Airflow) → DWH (ClickHouse or BigQuery) → dbt models → BI dashboard on Metabase. Courses: Karpov.Courses "Data Engineer" (includes DWH), OTUS "Data Engineer", Yandex.Practicum "Data Engineer", DataCamp "Data Engineer with dbt", Snowflake University (EN — free, best Snowflake resource), ClickHouse Academy (EN — free). Books: "The Data Warehouse Toolkit" Kimball (must-read), "Analytics Engineering with dbt" (modern data stack guide). Time to Junior DWH — 6-10 months (faster than general Data Engineer thanks to smaller scope).
How many DWH jobs are open across CIS and Europe?
171 active open Data Warehouse Engineer positions. Geography: 🇵🇱 Poland, 🇺🇸 USA, IN. Sources: hh.ru, Habr Career, getmatch, Djinni, LinkedIn (huge international DWH segment — Snowflake/dbt Labs/Databricks), Telegram (@data_engineering_jobs, @dataeng_search, @ODS Jobs, @ClickHouse_ru), NoFluffJobs/JustJoin.it (Poland), career pages of EPAM Data Practice / Luxoft / Andersen Data, Snowflake/dbt partner networks. The real market is broader thanks to the huge international remote (Snowflake/dbt Labs/Databricks — all full-remote-friendly). Time to close a Senior DWH role — 4-6 weeks.
What skills does a Senior DWH Engineer need?
A Senior DWH owns the full cloud-DWH cycle. SQL mastery: window functions deep, recursive CTE, query plans (EXPLAIN ANALYZE), partition pruning, indexes, materialised views, query-optimisation patterns. One cloud DWH deeply: Snowflake (Tasks/Streams/Time Travel/micro-partitions/Snowpipe/cost optimisation via warehouse sizing) or BigQuery (slot management/BQML/partitioned tables) or ClickHouse (ReplicatedMergeTree/distributed engines/materialised views/codecs/dictionaries). Modeling: Kimball mastery (star/snowflake schema, conformed dimensions, SCD types 1-6), Data Vault basics, lakehouse architecture (Iceberg/Delta partition evolution, time-travel, schema evolution). dbt: mastery (models/sources/tests/macros/snapshots/seeds/exposures), best-practices (incremental models, materialisation strategies, dbt-snowflake/dbt-bigquery adapter specifics). Performance: query-optimisation patterns per DWH engine, cost management (Snowflake credit usage / BigQuery slots / ClickHouse cluster sizing). Ingestion: Kafka + Debezium for CDC, Airflow for batch ingestion, Snowpipe / Snowflake Streams / materialised views. Data Quality: dbt tests + Great Expectations + Soda. BI integration: one of Looker/Mode/Metabase/Superset/Yandex DataLens at semantic-layer design level. Soft: code review (SQL + dbt), mentoring Middle, communication with Analyst/Business teams. English for Senior+ must — DWH documentation 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 23, 2026 at 5:12 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 Warehouse in IT: CIS and Europe market. Accessed: 7/23/2026. URL: https://zorky.tech/en/research/data