DWH Engineer

Gypsy Collective is a digital entertainment team with over 100 professionals working in a remote-first environment. The company focuses on creating scalable, reliable, and high-performance data warehouse solutions to support analytics and business decision-making.

• Strong SQL skills: complex queries, CTEs, window functions, analytical queries (5+ years experience)
• Knowledge of Python or other scripting languages for data transformations (4+ years experience)
• Deep understanding of DWH concepts: ETL/ELT, Data Vault, Kimball, Star/Snowflake schemas (5+ years experience)
• Experience with Airflow or other data pipeline orchestrators (5+ years experience)
• Hands-on experience with modern DWH and query engines: BigQuery, Snowflake, Redshift, ClickHouse, Vertica, AWS Athena, Trino (5+ 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 (3+ 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 such as AWS, GCP, or Azure
• Familiarity with data governance, data cataloging, and lineage tools

• Designing, building, and maintaining scalable Data Warehouse architectures aligned with business needs
• Developing and optimizing ETL/ELT pipelines using Python, Airflow, and custom solutions
• Working with DWH/Data Lake technologies including PostgreSQL, Trino, and BigQuery
• Implementing incremental loads, CDC, backfills, and reprocessing strategies
• Optimizing query performance, data models, and pipeline execution
• Ensuring data quality through validation, automated testing, monitoring, and alerting
• Integrating new data sources (APIs, third-party systems, raw data) without disrupting existing pipelines
• Collaborating with analysts, engineers, BI teams, and business stakeholders to translate requirements into scalable data solutions
• Reviewing code, mentoring engineers, and contributing to data engineering standards and best practices