Databricks Platform Engineer

Job Description

In this role, you will design, build, and enhance the enterprise Databricks platform to support analytics, data products, machine learning, and AI initiatives across the organization. You will collaborate with Enterprise Architecture, Data Engineering, Product Management, Cloud Engineering, and Data Science teams to develop reusable platform services, automation frameworks, engineering standards, and self-service capabilities that improve platform adoption, developer experience, and engineering productivity while advancing the organization's enterprise data and AI platform.

Responsibilities:

• Develop and enhance enterprise Databricks platform capabilities that support analytics, data products, machine learning, and AI workloads.
• Implement platform architectures, engineering standards, and reusable design patterns aligned with enterprise technology strategies.
• Build and maintain Infrastructure-as-Code solutions, CI/CD pipelines, and platform automation to improve consistency and operational efficiency.
• Create reusable templates, accelerators, and deployment patterns that enable self-service platform adoption and solution delivery.
• Optimize platform scalability, performance, reliability, and cost efficiency across cloud environments.
• Collaborate with Enterprise Architecture, Cloud Engineering, Data Engineering, Product Management, and Data Science teams to operationalize platform capabilities.
• Support platform services including MLflow, Model Registry, Feature Store, Vector Search, and related Databricks capabilities.
• Evaluate emerging Databricks features and cloud technologies through technical assessments and proof-of-concepts.
• Recommend platform enhancements, automation opportunities, and engineering best practices that improve developer experience and platform maturity.
• Contribute to the evolution of enterprise platform standards, reference architectures, and strategic roadmap initiatives.

Qualifications:

• Relevant degree preferred.
• 5 or more years of relevant experience required.
• 2 or more years of hands-on experience with the Databricks platform required.
• Experience with Azure cloud services and cloud-native architectures preferred.
• Experience developing and maintaining Infrastructure-as-Code solutions using Terraform and CI/CD pipelines.
• Strong knowledge of Delta Lake, Unity Catalog, Apache Spark, and lakehouse architecture concepts.
• Experience developing automation solutions and reusable engineering components.
• Experience with MLflow, MLOps, AI platform technologies, streaming, or event-driven architectures preferred.
• Strong analytical, problem-solving, collaboration, and communication skills.
• Experience supporting enterprise-scale data platforms and healthcare or other regulated environments preferred.