Data Engineer

We are a growing, fast-paced global startup building advanced data-driven products, and we're looking for a mid-level Data Engineer to help build, maintain, and scale our modern data and analytics platform. This is a hands-on, remote role based in Poland, where you'll work closely with analytics and product teams to turn raw data into reliable, well-modeled datasets that support strategic decision-making across the company. What You'll Do: - Build and maintain robust ETL data pipelines. - Develop, test, and document dbt models following engineering best practices. - Work extensively with Google BigQuery for analytics, optimization, and cost efficiency. - Write clean, maintainable Python code for data processing and automation. - Collaborate closely with cross-functional teams, communicating requirements and outcomes clearly. - Ensure data quality, reliability, and consistency, monitoring pipelines and troubleshooting end-to-end. - Contribute to data architecture decisions in a dynamic startup environment. Must-Have Requirements: - Hands-on experience with dbt (models, tests, documentation). - Experience working with Google BigQuery. - Solid Python skills for data engineering use cases. - Strong SQL skills and a solid understanding of data modeling concepts. - Experience building and maintaining production data pipelines. - Familiarity with Git and collaborative development workflows. - Strong English capabilities, both written and verbal. - A serious, responsible mindset with high motivation to succeed, learn, and grow. Nice-to-Have: - Experience with event-based data, funnels, retention, cohorts, and LTV. - Experience with data orchestration tools (Airflow, Prefect, Dagster). - Familiarity with data ingestion tools (Fivetran, Airbyte, or similar). - Exposure to GCP beyond BigQuery. - Prior experience working in a startup or fast-paced environment - Experience in the gaming industry What We Offer: - Fully remote work setup from Poland. - Opportunity to grow and take on significant ownership over time. - Modern data stack and engineering best practices. - Collaborative, low-bureaucracy startup environment. - Competitive compensation.