Data Engineer (Mid-Level)
Indexed description
What is your new project?:
- Domain: iGaming
- Location: Israel
- Company size: 34 employees
- Founded in: 2022
We are a growing gaming startup building data-driven products, and we’re looking for a mid-level Data Engineer to help build, maintain, and scale our data and analytics platform.
This is a hands-on role where you’ll work closely with analytics, product, and game teams to turn raw game and product data into reliable, well-modeled datasets that support decision-making across the company.
What Makes You a Great Fit
- 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 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
- Good communication skills and the ability to work effectively with cross-functional teams
- A serious, responsible mindset — takes ownership and accountability for work
- High motivation to succeed, learn, and grow in a startup environment
- Experience in the gaming industry (mobile, F2P, online, or real-time analytics)
- Understanding of 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
- Experience working in a startup or fast-paced environment
- Build and maintain ELT data pipelines for game and product data
- Develop, test, and document dbt models following analytics engineering best practices
- Work extensively with Google BigQuery for analytics, optimization, and cost efficiency
- Write clean, maintainable Python for data processing and automation
- Collaborate closely with analytics, product, and game teams — clearly communicating requirements, assumptions, and outcomes
- Ensure data quality, reliability, and consistency across datasets
- Monitor pipelines, troubleshoot issues, and take ownership of fixes end-to-end
- Contribute to data architecture decisions in a fast-paced startup environment
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