Senior Data Engineer

Scientific Games is the global leader in lottery games and technology, seeking a Senior Data Engineer to modernize their lottery data platform. The role involves building reliable data pipelines, improving data models, and ensuring high-quality data environments across legacy and cloud systems.

Responsibilities:

• Design, build, and operate reliable data pipelines that move lottery data from operational systems into warehouse and analytics environments
• Improve ingestion, transformation, modeling, orchestration, observability, data quality, lineage, and access patterns
• Help define and implement data contracts covering schema, quality, timeliness, lineage, storage, access, and customer or jurisdiction 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 so source data is usable, trusted, and well-understood
• Reduce KTLO work through automation, better monitoring, resilient pipeline design, infrastructure as code, and platform simplification
• Troubleshoot production data issues and help create durable fixes rather than recurring manual workarounds
• Support the transition from legacy approaches to a more scalable cloud- and AI-ready data architecture while protecting business continuity
• Mentor other engineers through design reviews, code reviews, documentation, pairing, and clear technical standards

Requirements:

• 7+ years of Data Engineering, Data Platform, or Data Warehousing experience
• Strong SQL and data modeling skills, including experience with analytical, reporting, and operational data use cases
• Experience with batch and streaming ingestion, ETL / ELT, orchestration, transformation frameworks, and data quality controls
• Experience working 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 and turning recurring problems into durable fixes
• 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; able to explain data behavior, tradeoffs, and risks to technical and non-technical partners
• Lottery, Gaming, Payments, Financial Systems, Regulated Transactional Systems, or other high-reliability data environments
• Multi-tenant, jurisdiction-specific, customer-specific, or contractually segmented data environments
• AWS, Databricks, Snowflake, Redshift, Glue, Spark, Airflow, dbt, Kafka, or similar data platform technologies
• Metadata-driven or configuration-driven onboarding for new sources, jurisdictions, products, or customers