Jobs and Careers

Senior Data Engineer

Alpharetta, United Statesfull_timeVerifiedPosted 12 Jun 2026

About the role

Scientific Games:

Scientific Games is the global leader in lottery games, sports betting and technology, and the partner of choice for government lotteries. From cutting-edge backend systems to exciting entertainment experiences and trailblazing retail and digital solutions, we elevate play every day. We push game designs to the next level and are pioneers in data analytics and iLottery. Built on a foundation of trusted partnerships, Scientific Games combines relentless innovation, legendary performance, and unwavering security to responsibly propel the global lottery industry ever forward.

Position Summary

Scientific Games is hiring a Senior Data Engineer to help build and modernize the lottery data platform supporting reporting, analytics, data science, and future AI capabilities.

You’ll build and operate reliable data pipelines, improve data models, and ensure high-quality, scalable warehouse environments across legacy and cloud systems.

The ideal candidate is an experienced builder who improves data models, defines data contracts, and automates processes to reduce operational overhead. They prioritize data quality, reliability, and cost efficiency while developing a strong understanding of the lottery domain and its impact.

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

What Success Looks Like

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

Experience That Fits

5-7+ years of Data Engineering, Data Platform, or Data Warehousing experience
Extensive SQL and data modeling skills, including experience with analytical, reporting, and operational data use cases.
Strong background working in AWS or similar cloud environments
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.

Especially Useful Backgrounds

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.
Databricks, Snowflake, Python, Redshift, Glue, Spark, Airflow, dbt, Kafka, or similar data p