Cloud Data Engineer

Cloud Data Engineer

Company:

The Boeing Company

Boeing Defense, Space and Security (BDS) is seeking an Advanced Information Technologist – Cloud Data Engineer to join our Data Analytics Engineering & Integration team in Ridley Park, PA; Seattle, WA; Arlington, VA; San Diego, CA; Long Beach, CA; Mesa, AZ; or Hazelwood, MO. The Solutions team is responsible for ensuring that solution datasets are discoverable, secure, performant, and reliably delivered to downstream consumers to enable analytics, operations, and program decisioning across BDS..

Role summary:

• As an Advanced Information Technologist – Cloud Data Engineer focused on the solutions space, you will own both DBaaS/platform lifecycle and consumer-facing dataset delivery. You will provision and operate DBaaS instances, implement dataset provisioning and access patterns, optimize query performance, enforce security/compliance (including GovCloud constraints), and collaborate with data architects to design, operationalize, and govern enterprise data ontologies and canonical models that enable consistent semantics and discoverability.

Position Responsibilities

• Participate in all aspects of agile delivery, including design, implementation, testing, and deployment of solution‑space features and operational tooling.

• Own provisioning, configuration, scaling, tuning, patching, backup/restore, and lifecycle management of DBaaS instances that host solution data (e.g., RDS/Aurora or equivalent), including capacity planning, disaster recovery, and incident response.

• Implement and maintain dataset provisioning and delivery processes (cataloging, access controls, dataset packaging, freshness/latency SLAs) to support downstream consumers and integrations.

• Optimize consumer performance and cost‑to‑serve through data layout and query optimization (indexing, partitioning, materialized views, caching) and by advising solution owners on access/query patterns.

• Design, implement, and operationalize enterprise data ontologies and canonical models: create semantic models, map solution datasets to ontologies, enforce taxonomy/versioning, and partner with governance for discoverability and lineage.

• Design and enforce authorization models: implement RBAC for role-level permissions and ABAC for fine‑grained, attribute‑driven policies (dataset sensitivity, clearance, environment, ontology tags), integrated via a centralized policy engine.

• Build and maintain Infrastructure‑as‑Code (Terraform) and CI/CD for DB and dataset lifecycle changes; automate entitlement provisioning and deprovisioning workflows; author runbooks and participate in on‑call rotations.

• Enforce security, compliance, and GovCloud requirements: manage RBAC/ABAC controls, encryption at rest/in transit, auditing and immutable logging, data classification/tags, masking/anonymization where required, and periodic access attestation.

• Integrate policy & observability: centralize policy evaluation, instrument audit logs and alerts for policy decisions and anomalous access, and include policy tests in pipelines.

• Design, deploy, and maintain data integrations and operational patterns within enterprise data platforms (e.g., Palantir Foundry) where applicable, including dataset modeling, Foundry Ontology alignment, transforms, and operationalization.

• Provide stakeholder support: advise solution owners on SLAs, access patterns, and best practices; validate consumer requirements; perform dataset handoffs and document usage guides.

• Continuously review and recommend platform improvements to improve reliability, security, performance, and cost efficiency.

Work arrangement and clearance

• This position is hybrid. The selected candidate will be required to perform 1 day per week onsite at one of the listed location options. This requirement is at the hiring team’s discretion and could change in the future.

• This position requires the ability to obtain a U.S. Security Clearance for which the U.S. Government requires U.S. Citizenship. An interim and/or final U.S. Secret Clearance Post-Start is preferred.