DWH Engineer job opportunity at Gypsy Collective.

Home Jobs In Poland Tag: Job DWH Engineer position at Gypsy Collective


Date2026-02-11T19:12:03.991Z bot
Gypsy Collective DWH Engineer
Experience: 5-years
Pattern: Full-time
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degreeMBA
Poland

We are looking for a Middle/Senior DWH Engineer to design, build, and maintain scalable data warehouse solutions that power analytics and business decision-making. You will work across the full data lifecycle — from ingestion and modeling to optimization, reliability, and automation — collaborating closely with analysts, developers, and business stakeholders. Strong SQL skills: complex queries, CTEs, window functions, analytical queries (5+ years experience); Knowledge of Python or other scripting languages for data transformations (3+ years experience); Deep understanding of DWH concepts: ETL/ELT, Data Vault, Kimball, Star/Snowflake schemas (4+ years experience); Experience with Airflow or other data pipeline orchestrators (3+ years experience); Hands-on experience with modern DWH and query engines: BigQuery, Snowflake, Redshift, ClickHouse, Vertica, AWS Athena, Trino (2+ years experience); Confident use of Git; experience with team workflows (pull requests, rebasing, merge conflict resolution) (5+ years experience); Understanding of server and cloud infrastructure: basic skills in configuration, maintenance, monitoring, and load control (2+ years experience). Nice to Have Experience with CDC tools and streaming data sources; Knowledge of Docker, Kubernetes, and Infrastructure as Code (Terraform); Experience with cloud platforms: AWS, GCP, or Azure; Familiarity with data governance, data cataloging, and lineage tools. Responsibilities: Design, build, and maintain scalable Data Warehouse architectures aligned with business needs; Develop and optimize ETL/ELT pipelines using Python/Airflow and custom solutions; Work with DWH\Datalake: PostgreSQL, Trino, BigQuery; Implement incremental loads, CDC, backfills, and reprocessing strategies; Optimize query performance, data models, and pipeline execution; Ensure data quality through validation, automated testing, monitoring, and alerting; Integrate new data sources (APIs, third-party systems, raw data) without disrupting existing pipelines; Collaborate with analysts, engineers, BI teams, and business stakeholders to translate requirements into data solutions; Mentor engineers, review code, and contribute to data standards and best practices.

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