Manager, Data Engineering

IGT, a Nevada Corporation•Chicago, IL

11d•$55,177 - $156,700

About The Position

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 - www.igt.com.

Requirements

Bachelor’s degree in computer science, engineering, information systems, data science, or a related field, or equivalent professional experience.
Significant experience in data engineering, including experience designing and supporting production data pipelines.
Previous experience leading or managing data engineers, technical teams, or major data delivery workstreams.
Strong hands-on experience with Databricks, Apache Spark, PySpark, and SQL.
Experience building data solutions using lakehouse or medallion architecture patterns.
Strong understanding of dimensional modeling, relational modeling, data warehousing, and analytical data product design.
Experience working with cloud data platforms, preferably AWS.
Experience with Git-based development, pull requests, code reviews, CI/CD, and controlled production deployment.
Experience integrating data from enterprise applications, relational databases, APIs, and file-based sources.
Demonstrated ability to operate effectively in environments with ambiguity, changing priorities, legacy systems, and complex dependencies.
Strong written and verbal communication skills.
Ability to work directly with business stakeholders and translate business processes into technical requirements.

Nice To Haves

Experience with AWS services such as S3, IAM, networking, Secrets Manager, Lambda, or related data services.
Experience with Databricks Unity Catalog, Workflows, Delta Lake, Delta Live Tables or Lakeflow Declarative Pipelines, and Databricks Asset Bundles.
Experience with SAP data, including SAP ECC, S/4HANA, HANA, finance, supply chain, or operational data domains.
Familiarity with Python software engineering practices, reusable framework development, and automated testing.

Responsibilities

Lead, coach, and develop a team of data engineers, establishing clear expectations for delivery, technical quality, documentation, and production support.
Own the planning and delivery of data engineering initiatives across assigned business domains, balancing modernization, integration, operational support, and stakeholder priorities.
Translate complex business requirements into scalable, reliable, and maintainable data pipelines, models, and data products.
Provide hands-on technical leadership through architecture reviews, code reviews, troubleshooting, and guidance on Databricks, Spark, SQL, and lakehouse design.
Establish and enforce engineering standards for bronze, silver, and gold data layers, data modeling, reusable frameworks, testing, source control, and CI/CD.
Lead the migration and integration of data from legacy platforms, SAP, Salesforce, operational databases, APIs, and file-based processes into the AWS Databricks environment.
Ensure data solutions include appropriate quality controls, reconciliation, observability, lineage, security, RBAC, and production monitoring.
Partner with business, analytics, architecture, application, and infrastructure teams to clarify requirements, manage dependencies, and communicate delivery risks and tradeoffs.
Maintain continuity for business-critical reporting and operational data processes during system migrations and organizational change.
Promote ownership and accountability throughout the full engineering lifecycle, including requirements, development, deployment, documentation, incident resolution, and knowledge transfer.