Data Warehouse: market

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

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.

Top 5: snowflake, cloud, sql, python, azure. 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 — 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. 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.

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.

Yes, 43% of DWH jobs are remote or hybrid. Work is fully cloud-based (Snowflake/BigQuery/ClickHouse Cloud). Russian product companies — 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%.

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).

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).

353 active open Data Warehouse Engineer positions. Geography: Russia / Ukraine / Poland. 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.

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.