Director of Engineering - Data Platforms

Vizient•Chicago, IL

3d•$135,200 - $236,600

About The Position

When you’re the best, we’re the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future.

Requirements

Define and execute enterprise data platform strategy, architecture, and multi-year roadmaps with a focus on Azure Databricks, Microsoft SQL Server, and related Azure data technologies.
Lead platform engineering and administration teams to deliver reliable, secure, scalable, observable, and high-performing enterprise data services.
Drive Azure Databricks platform engineering, including platform architecture, administration, Unity Catalog, Delta Lake, MLflow, access controls, workload management, automation, observability, performance, and cost optimization.
Establish Platform-as-a-Product practices, reusable engineering patterns, Infrastructure-as-Code, CI/CD automation, and self-service capabilities that improve engineering productivity and platform adoption.
Lead modernization of enterprise data platforms and migration of appropriate workloads to Azure and Lakehouse architectures while strengthening resilience, disaster recovery, and operational readiness.
Establish platform governance, security, data classification, lineage, auditing, access management, and compliance controls in collaboration with Data Governance, Security, IAM, and Enterprise Architecture teams.
Enable scalable platform capabilities for analytics, data products, machine learning, AI, and operational reporting.
Establish service-level objectives, operational metrics, monitoring, incident management, capacity planning, and continuous improvement practices.
Manage platform investments, budgets, capacity forecasts, vendor relationships, licensing, and FinOps practices to optimize technology consumption and operational costs.
Develop and mentor engineering leaders and technical teams while fostering collaboration, automation, innovation, and operational excellence.

Nice To Haves

Lead evaluation and adoption of emerging data, AI, and cloud technologies through proofs of concept and strategic platform investments.
Advance platform standardization and consolidation opportunities that reduce technical complexity and improve scalability.
Partner with Data Engineering and Data Science teams to accelerate delivery of enterprise data products, machine learning, and AI capabilities.

Responsibilities

Define and execute enterprise data platform strategy, architecture, and multi-year roadmaps with a focus on Azure Databricks, Microsoft SQL Server, and related Azure data technologies.
Lead platform engineering and administration teams to deliver reliable, secure, scalable, observable, and high-performing enterprise data services.
Drive Azure Databricks platform engineering, including platform architecture, administration, Unity Catalog, Delta Lake, MLflow, access controls, workload management, automation, observability, performance, and cost optimization.
Establish Platform-as-a-Product practices, reusable engineering patterns, Infrastructure-as-Code, CI/CD automation, and self-service capabilities that improve engineering productivity and platform adoption.
Lead modernization of enterprise data platforms and migration of appropriate workloads to Azure and Lakehouse architectures while strengthening resilience, disaster recovery, and operational readiness.
Establish platform governance, security, data classification, lineage, auditing, access management, and compliance controls in collaboration with Data Governance, Security, IAM, and Enterprise Architecture teams.
Enable scalable platform capabilities for analytics, data products, machine learning, AI, and operational reporting.
Establish service-level objectives, operational metrics, monitoring, incident management, capacity planning, and continuous improvement practices.
Manage platform investments, budgets, capacity forecasts, vendor relationships, licensing, and FinOps practices to optimize technology consumption and operational costs.
Develop and mentor engineering leaders and technical teams while fostering collaboration, automation, innovation, and operational excellence.
Lead evaluation and adoption of emerging data, AI, and cloud technologies through proofs of concept and strategic platform investments.
Advance platform standardization and consolidation opportunities that reduce technical complexity and improve scalability.
Partner with Data Engineering and Data Science teams to accelerate delivery of enterprise data products, machine learning, and AI capabilities.