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# Engineer Data II

- **Company:** International Game Technology Inc
- **Location:** Reno, NV, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Microsoft Excel, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Cloud Engineering, Code Review, Information Systems, Databases, Continuous Integration, Data as a Services, Data Architecture, Data Cleansing, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Data Warehousing, Distributed Computing Environment, Identity and Access Management, JSON, Python (Programming Language), Machine Learning, SQL Databases, Workflow Management Systems, Enterprise Data Management, Enterprise Software Applications, Data Storage Technologies, Cloud Platform System, Data Ingestion, Apache Spark, Git, Data Lakes, Semi-structured Data, Information Technology, Machine Learning Operations, Functional Programming, Restful APIs, Data Pipelines, Databricks
- **Published:** October 14, 2026
- **Apply:** https://jobs.igt.com/talentcommunity/apply/1374762957/?locale=en_US

## About the Role

* 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 SQL development skills.
 * Strong Python programming experience.
 * Experience building data pipelines using Apache Spark and Databricks (or similar distributed data processing platforms).
 * Experience developing modern ETL/ELT pipelines.
 * 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.

Preferred

 * Experience with AWS data services such as Amazon S3, IAM, Lambda, Glue, 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 or data preparation for machine learning and generative AI applications.
 * Experience working in gaming, hospitality, financial services, or other data-intensive industries.

## Description

The Data Engineer II builds and maintains scalable, cloud-native data solutions within the Global Data Platform & Engineering team, supporting enterprise analytics, reporting, and emerging AI initiatives. The role uses Databricks, Apache Spark, Delta Lake, Python, SQL, and AWS to develop reliable data pipelines and curated datasets. Working with business stakeholders, analytics teams, and fellow engineers, the position translates business requirements into high-quality, production-ready data solutions while solving complex data challenges 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.