Инженер данных

Scientific Games is a global leader in lottery games, sports betting, and technology, serving as a partner for government lotteries. The company develops backend systems, entertainment experiences, and retail and digital solutions, while pioneering advancements in data analytics and iLottery.

• Design, build, and operate reliable data pipelines to move lottery data from operational systems into warehouse and analytics environments;
• Improve ingestion, transformation, modeling, orchestration, observability, data quality, lineage, and access patterns;
• Define and implement data contracts covering schema, quality, timeliness, lineage, storage, access, and constraints;
• Build repeatable onboarding patterns for new jurisdictions, games, data sources, and reporting needs;
• Partner with DBA, IT, product, application engineering, analytics, BI, and data science teams to ensure data is usable and trusted;
• Reduce manual work through automation, monitoring, resilient pipeline design, infrastructure as code, and platform simplification;
• Troubleshoot production data issues and create durable fixes;
• Support the transition from legacy approaches to a scalable cloud- and AI-ready data architecture;
• Mentor other engineers through design reviews, code reviews, documentation, and technical standards.

• 5-7+ Years of Data Engineering, Data Platform, or Data Warehousing experience;
• Extensive SQL and data modeling skills for analytical, reporting, and operational use cases;
• Strong background in AWS or similar cloud environments;
• Experience with batch and streaming ingestion, ETL/ELT, orchestration, transformation frameworks, and data quality controls;
• Experience with data contracts, schema evolution, lineage, observability, access controls, and service-level expectations;
• Experience improving legacy data platforms while maintaining production continuity;
• Experience troubleshooting complex production data issues;
• Experience with cloud data platforms, distributed processing, infrastructure as code, and modern data engineering practices;
• Ability to work with cross-functional teams;
• Clear written and verbal communication skills;
• Nice to have: Experience in lottery, gaming, payments, financial systems, or regulated transactional systems; experience with multi-tenant or jurisdiction-specific data environments; knowledge of Databricks, Snowflake, Python, Redshift, Glue, Spark, Airflow, dbt, or Kafka; experience with metadata-driven onboarding.

• Employees may be required to obtain gaming or other licenses, undergo background investigations, or security checks;
• Consent to a due diligence/background investigation is a prerequisite to employment.