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Databricks

Builds cloud software for data analysis and AI

Staff Production Engineer-Public Sector

Cloud EngineeringConfirmed live in the last 24 hours
Full-timeVirginia, USAIn-person

About the Job

Requirements

  • Candidates must have an active Top Secret/Sensitive Compartmented Information (TS/SCI) security clearance.
  • Candidates must have a BS, MS, or PhD in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
  • Staff-level candidates must have 8 or more years of experience operating and automating large-scale cloud environments, with a track record of driving cross-team infrastructure improvement.
  • Candidates must have deep hands-on experience with at least one major cloud provider (AWS, Azure, or GCP) in areas such as identity and access management, networking, accounts, subscriptions, projects, and audit logging.
  • Candidates must have a strong background in infrastructure as code and automation, such as Terraform, CloudFormation, or similar tools, and continuous integration and continuous delivery for infrastructure changes.
  • Candidates must have proven experience working in or with security-sensitive or regulated environments, such as SOC 2, FedRAMP, ISO 27001, financial services, or the public sector, and translating requirements into concrete technical controls.
  • Candidates must be familiar with access review processes, policy baselines, and audit evidence for cloud environments.
  • Candidates must have demonstrated success running high-availability, security-critical services, including on-call responsibilities and incident management.
  • Candidates must have strong debugging and problem-solving skills across distributed systems and be able to navigate ambiguous issues spanning multiple teams and platforms.

Responsibilities

  • Design, automate, and operate the identity and access management, account/subscription, and project lifecycle across AWS, Azure, and GCP, enforcing least-privilege and standardized access patterns at scale.
  • Review, implement, and continuously improve cloud identity and access policies, including IAM, Okta, and Opal, to align with Databricks security standards and audit requirements.
  • Build and maintain reliable, observable automation and tooling to apply cloud changes such as roles, policies, accounts, and networking safely and repeatedly.
  • Treat operational and security issues as software problems by eliminating toil, driving root-cause analysis, and codifying fixes into infrastructure and tooling.
  • Own and improve security and audit logging data pipelines from cloud providers into internal systems, ensuring timely and accurate data for detection, investigations, and audits.
  • Partner with Security, Compliance, and Audit teams to provide evidence, clarifications, and policy updates that keep environments aligned with evolving standards.
  • Operate and improve specialized, highly regulated environments such as FedRAMP and GovCloud, including release management, patching cadences, and support for secure access workflows such as secure admin workstations.
  • Ensure high availability and resiliency for critical security and access infrastructure across regulated environments.
  • Participate in a 24x7 on-call rotation for high-severity incidents impacting cloud accounts, IAM, or security data pipelines.
  • Partner with product engineering, security engineering, and field teams during incidents to restore service and harden systems for the future.

Desired Qualifications

  • Possession of a current Counterintelligence or Full Scope polygraph is highly desired and considered a significant plus.
  • Experience with Okta, Opal, or similar identity/access tooling.
  • Background operating secure admin workstations or comparable hardened access patterns.
  • Experience migrating cloud accounts or subscriptions during mergers and acquisitions or large-scale reorganizations.

About the Company

Databricks

Builds cloud software for data analysis and AI

Databricks builds cloud software that businesses use to organize data, analyze it, and develop AI applications. Its lakehouse architecture brings the flexible storage of a data lake together with the querying and management tools of a data warehouse. Data engineers prepare and update datasets, analysts build reports, and developers train models or create applications in a shared workspace with controls over who can use the data. The platform runs on Amazon, Microsoft, and Google clouds, and customers pay for computing usage or commit to longer contracts for discounted rates. Databricks was founded in 2013 and is headquartered in San Francisco, California.

Company size
10,001+
Company stage
Late Stage VC
Total funding
$32.1B
Headquarters
San Francisco, CA
Founded
2013

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