Analytics Platform Engineer / Data Engineer

JOB DESCRIPTION
As an Analytics Platform Engineer supporting Azure & AWS Databricks from a DevOps perspective requires a hybrid skill set spanning primarily cloud infrastructuremanagement and deployment, automation and CI/CD practices and it is helpful if they a background or exposure to ELT and data engineering and Database AdministrationConcepts etc. 
 
The resource will be responsible for developing the Databricks foundation for use within U.S. Bank as an Analytics Platform Engineering team member, where they  will develop automation via Terraform modules or other tooling to deploy Databricks Workspaces, along with other cloud resources, we are developing a platform on top of a PaaS offering where you also need to implement a CICD solution in Gitlab.
  
 
Required Skills:

• 4+ years of experience developing APIs and integrating with 3rd party APIs.
• Strong SQL skills (SQL Server or Oracle). Familiarity with scripting languages (Python, Bash, PowerShell).
• Experience with version control systems (Git, GitHub, GitLab).
• Knowledge of CI/CD pipelines and DevOps best practices.
• Understanding of workflow automations is a plus. Excellent problem-solving, analytical, and communication skills.
• Azure AD, OAuth, JWT, Microsoft Teams SDK, Building apps for teams, Azure hands with AD B2C, BOT, SignalR, AKS, App Registrations, App Insights
• Azure Databricks
• CI/CD: Jenkins, Terraform, GitLab CI/CD
• Terraform Module Development

• Implement CI/CD Pipelines: Design and maintain continuous integration and continuous deployment pipelines using tools like Gitlab, Azure DevOps or GitHub Actions for deploying data pipelines and infrastructure changes.
• Infrastructure as Code (IaC): Provision and manage Azure Analytics services (Databricks, etc.) through code using tools such as Terraform, ARM templates, or Bicep to ensure consistency and repeatability.
• Platform Operations: Deploy, manage, and optimize the performance and resource utilization of Azure data services, including monitoring and troubleshooting data pipeline failures and performance issues.
• Automation & Scripting: Automate routine operational tasks and application deployments using scripting languages like Python, PowerShell, or Bash.
• Security & Compliance: Implement security best practices, including identity and access management (IAM), data encryption, and compliance with data governance policies (e.g., GDPR) within the platform.
• Collaboration: Work closely with data engineers, data scientists, and business analysts to translate data requirements into robust technical solutions and foster a DevOps culture within the organization
• Change Management Process: Change Requests, CLearPATH, Incidents, Vulnerabilities, Problem Records.