Middle DWH Engineer
Gypsy Collective • Ukraine
No Relocation
Posted: June 2, 2026
Job Description
We are looking for a Middle DWH Engineer to design, build, and maintain scalable data warehouse solutions that support analytics and business decision-making. You will work across the full data lifecycle - from data ingestion and transformation to orchestration and optimization - while collaborating with analysts, engineers, and business stakeholders.
📅 Your adventures include:
- design, develop, and maintain DWH and Data Lake solutions aligned with business requirements;
- build, optimize, and support ETL/ELT pipelines using Python, SQL, Airflow, and related technologies;
- integrate and maintain data flows from APIs, databases, SaaS platforms, and third-party systems;
- work with PostgreSQL, Trino, and cloud-based analytical platforms to support reporting and analytics needs;
- implement and support incremental loading, CDC processes, backfills, and reprocessing workflows;
- monitor, troubleshoot, and improve data pipelines to ensure reliability, stability, and timely delivery of data;
- implement data quality checks, validation rules, and automated monitoring processes;
- optimize SQL queries, data models, and pipeline performance;
- collaborate with analysts, BI developers, engineers, and business stakeholders to deliver scalable data solutions;
- participate in code reviews and contribute to engineering standards and best practices.
🧭 What makes you a great match:
- strong SQL skills, including complex queries, CTEs, window functions, analytical queries, and query optimization (3+ years);
- strong experience integrating external data sources, including REST APIs, databases, SaaS platforms, and third-party systems; ability to design, develop, troubleshoot, and maintain reliable data ingestion processes in production environments (3+ years);
- experience with Apache Airflow or similar workflow orchestration tools (3+ years);
- experience with Python for data processing, integrations, and automation (2+ years);
- good understanding of DWH concepts, including ETL/ELT, dimensional modeling, Data Vault, and Star/Snowflake schemas (3+ years);
- experience building and supporting production data pipelines with a focus on reliability, stability, monitoring, and error handling;
- understanding of data quality principles, including validation, testing, reconciliation, and monitoring;
- hands-on experience with PostgreSQL and Trino in production environments;
- experience with modern analytical databases or cloud data warehouses (such as BigQuery, Snowflake, Redshift, ClickHouse, Vertica, Athena, or similar platforms.);
- experience with Git and collaborative development workflows (pull requests, code reviews, rebasing, merge conflict resolution).
⭐ Nice to Have
- experience with Change Data Capture (CDC) tools and incremental loading strategies;
- experience with cloud platforms such as AWS, GCP, or Azure;
- familiarity with Docker and containerized environments;
- experience with streaming or near real-time data pipelines (Kafka);
- familiarity with dbt or similar data transformation frameworks;
- experience with data cataloging, lineage, or governance tools.
Additional Content
We are looking for a Middle DWH Engineer to design, build, and maintain scalable data warehouse solutions that support analytics and business decision-making. You will work across the full data lifecycle - from data ingestion and transformation to orchestration and optimization - while collaborating with analysts, engineers, and business stakeholders.
📅 Your adventures include:
- design, develop, and maintain DWH and Data Lake solutions aligned with business requirements;
- build, optimize, and support ETL/ELT pipelines using Python, SQL, Airflow, and related technologies;
- integrate and maintain data flows from APIs, databases, SaaS platforms, and third-party systems;
- work with PostgreSQL, Trino, and cloud-based analytical platforms to support reporting and analytics needs;
- implement and support incremental loading, CDC processes, backfills, and reprocessing workflows;
- monitor, troubleshoot, and improve data pipelines to ensure reliability, stability, and timely delivery of data;
- implement data quality checks, validation rules, and automated monitoring processes;
- optimize SQL queries, data models, and pipeline performance;
- collaborate with analysts, BI developers, engineers, and business stakeholders to deliver scalable data solutions;
- participate in code reviews and contribute to engineering standards and best practices.
🧭 What makes you a great match:
- strong SQL skills, including complex queries, CTEs, window functions, analytical queries, and query optimization (3+ years);
- strong experience integrating external data sources, including REST APIs, databases, SaaS platforms, and third-party systems; ability to design, develop, troubleshoot, and maintain reliable data ingestion processes in production environments (3+ years);
- experience with Apache Airflow or similar workflow orchestration tools (3+ years);
- experience with Python for data processing, integrations, and automation (2+ years);
- good understanding of DWH concepts, including ETL/ELT, dimensional modeling, Data Vault, and Star/Snowflake schemas (3+ years);
- experience building and supporting production data pipelines with a focus on reliability, stability, monitoring, and error handling;
- understanding of data quality principles, including validation, testing, reconciliation, and monitoring;
- hands-on experience with PostgreSQL and Trino in production environments;
- experience with modern analytical databases or cloud data warehouses (such as BigQuery, Snowflake, Redshift, ClickHouse, Vertica, Athena, or similar platforms.);
- experience with Git and collaborative development workflows (pull requests, code reviews, rebasing, merge conflict resolution).
⭐ Nice to Have
- experience with Change Data Capture (CDC) tools and incremental loading strategies;
- experience with cloud platforms such as AWS, GCP, or Azure;
- familiarity with Docker and containerized environments;
- experience with streaming or near real-time data pipelines (Kafka);
- familiarity with dbt or similar data transformation frameworks;
- experience with data cataloging, lineage, or governance tools.