Инженер данных

The company is an international product organization that develops SaaS products, AI-powered solutions, and modern web and mobile applications for global markets.

• Design, build, and maintain scalable ELT/ETL pipelines integrating product data, payment providers, and third-party APIs;
• Architect and own the cloud data warehouse;
• Build reliable orchestration workflows;
• Optimize warehouse performance and cost through efficient data modeling and query optimization;
• Develop clean, testable transformation layers;
• Design a semantic layer that provides consistent business metrics across the organization;
• Implement data quality testing, monitoring, lineage, and documentation;
• Build security and governance into the platform, including access controls, PII handling, and privacy-aware data practices;
• Work closely with Product and Growth teams to support product analytics, experimentation, subscription metrics, and business reporting;
• Collaborate with software engineers on event tracking, data contracts, and API integrations;
• Promote DataOps best practices, including CI/CD, version control, testing, and documentation-as-code.

• 3+ Years of experience as a Data Engineer or in a similar role;
• Strong SQL skills and proficiency in Python;
• Hands-on experience with modern cloud data warehouses such as Snowflake, BigQuery, or Redshift;
• Experience with workflow orchestration tools including Airflow, Prefect, Dagster, or similar;
• Strong understanding of data modeling and experience with dbt or comparable transformation frameworks;
• Experience designing and maintaining production-grade data pipelines;
• Strong sense of ownership and commitment to building reliable, high-quality data systems;
• Ability to communicate technical concepts clearly to cross-functional stakeholders;
• Experience implementing secure, privacy-conscious data practices;
• Nice to have: Experience in a B2C SaaS, subscription, or marketplace business, familiarity with Segment, Amplitude, Mixpanel, or similar product analytics platforms, experience designing semantic layers or canonical data models, exposure to streaming technologies such as Kafka or Kinesis, experience with ML infrastructure or feature stores, previous experience building a data platform in a startup or scale-up environment, Scala.

• Competitive compensation;
• 22 Paid vacation days plus local public holidays;
• Modern engineering environment with contemporary technologies;
• Opportunity to help shape a growing Data function from an early stage;
• Meaningful technical challenges with room to influence architecture and engineering practices;
• Collaborative, product-focused team.