Data Engineer II — Cloud Data Pipelines (Databricks​/AWS

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Chicago, IL, US, 60661 Providence, RI, US, 02903 Austin, TX, US, 78727 Las Vegas, NV, US, 89113 Reno, NV, US, 89521

Requisition

IGT, where innovation meets entertainment on a global scale! From the casino floor to your mobile screen, we deliver thrilling, responsible, and unforgettable gaming experiences—powered by world‑class content, strong technical and commercial capabilities and nurtured by a culture of collaboration, accountability, and ownership.

Whether it’s spinning reels, placing bets, or enabling secure payments, we turn innovation into impact through disciplined execution and long‑term value creation. With a team of over 6,000 employees across 30+ countries and products delivered in more than 100 jurisdictions worldwide, we operate at scale while staying closely connected to costumers we serve. If you’re ready to bring your talent to a team shaping the future of entertainment, your next big move starts here - .

We are seeking a Data Engineer II to join our Global Data Platform & Engineering team. In this role, you will help build and maintain scalable, cloud-native data solutions that power enterprise analytics, reporting, and emerging AI initiatives.

You will work with modern data technologies including Databricks, Apache Spark, Delta Lake, Python, SQL, and AWS to develop reliable data pipelines and curated datasets. Working closely with business stakeholders, analytics teams, and fellow engineers, you'll transform business requirements into high-quality, production-ready data solutions.

This position is ideal for an engineer who enjoys solving complex data challenges, building scalable solutions, and continuously learning new technologies.

• Design, develop, and maintain scalable ELT/ETL pipelines using Databricks, Apache Spark, SQL, and Python.
• Build and maintain reliable, high-quality datasets that support enterprise analytics, operational reporting, and AI use cases.
• Develop data transformations using Delta Lake and Lakehouse architecture best practices.
• Integrate data from enterprise applications, APIs, databases, files, and other internal and external sources.
• Collaborate with business stakeholders, analysts, and product teams to understand data requirements and deliver scalable solutions.
• Monitor, troubleshoot, and optimize production data pipelines for reliability, scalability, and performance.
• Participate in migrating and modernizing legacy data processes to cloud-native architectures.
• Improve data quality through validation, testing, monitoring, and automation.
• Contribute to engineering best practices through code reviews, documentation, and knowledge sharing.
• Support continuous improvement of the enterprise data platform by identifying opportunities for automation, optimization, and simplification.

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience.
• 3+ years of experience in Data Engineering or a related technical role.
• Strong Python programming experience.
• Experience building data pipelines using Apache Spark and Databricks (or similar distributed data processing platforms).
• Understanding of data modeling and data warehousing concepts.
• Experience working with cloud-based data platforms, preferably AWS.
• Familiarity with Delta Lake or similar modern data storage technologies.
• Experience using Git and collaborative software development practices.
• Strong analytical, troubleshooting, and problem-solving skills.
• Excellent communication and collaboration skills.

• Experience with AWS data services such as Amazon S3, Lambda, or related technologies.
• Experience working with Databricks on AWS.
• Experience with Delta Lake and Medallion/Lakehouse architecture.
• Experience with orchestration tools such as Databricks Workflows, Apache Airflow, or similar workflow orchestration platforms.
• Experience consuming and developing REST APIs.
• Experience working with semi-structured data, including JSON and Excel-based data ingestion.
• Familiarity with CI/CD practices for data engineering.
• Exposure to AI/ML workflows…