Senior Data Engineer

Position Summary Scientific Games is hiring a Senior Data Engineer to help build and modernize the lottery data platform that supports reporting, analytics, data science, and future AI capabilities. You will build and operate reliable data pipelines, improve data models, and ensure high‑quality, scalable warehouse environments across legacy and cloud systems. What This Person Will Do 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. Qualifications 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 working in AWS or a similar cloud environment. 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 and to communicate clearly with technical and non‑technical stakeholders. Knowledge of lottery, gaming, payments, financial systems, or regulated transactional systems is highly desirable. Experience in multi‑tenant, jurisdiction‑specific data environments is highly desirable. Hands‑on experience with Databricks, Snowflake, Python, Redshift, Glue, Spark, Airflow, dbt, Kafka, or similar technologies. Experience with metadata‑driven or configuration‑driven onboarding of new sources, jurisdictions, products, or customers. Success Metrics Make pipelines more observable, with earlier failure detection and clearer data quality standards. Establish repeatable patterns for onboarding new data sources. Increase confidence in data accuracy, lineage, timeliness, and usability. Apply strong business context to technical decisions across reporting, analytics, and AI use cases. Continuously improve systems by enhancing tests, monitoring, lineage, documentation, and reducing manual effort. Equal Employment Opportunity SG is an Equal Opportunity Employer and does not discriminate against applicants due to race, color, sex, age, national origin, religion, sexual orientation, gender identity, veteran status, disability, or any other federal, state, or local protected class. #J-18808-Ljbffr