Data Architect, Advisor

Peraton is looking to hire a Data Architect in the Washington DC Metro area.  This role will be a remote position.   At times, the role will also require travel to the Quantico client site when necessary.

The Data Architect defines the enterprise data architecture supporting operational systems, analytical platforms, and AI initiatives. The role is central to the Enterprise Data Layer (EDL), which brings together operational data stores and a data lake under common governance. The Data Architect establishes the event-driven integration patterns, governance and metadata standards, and reusable data products that enable secure, attribute-based access for enterprise teams, agency components, and authorized industry partners. Assess legacy systems to understand data structures, data quality, storage mechanisms, and transformation logic.

The Data Architect works closely with the Systems Architect on enterprise direction, with Solutions Architects on product-level data needs, and with Data Engineers who implement the patterns the role defines.

Primary Responsibilities

Enterprise Data Architecture

• Define the target-state data architecture spanning operational stores (relational and non-relational) and the analytical data lake.
• Develop conceptual, logical, and physical data models that support both transactional systems and analytics.
• Establish standards for how data moves from operational systems into the EDL and how it is curated for downstream use.
• Define how operational and analytical workloads are separated while keeping data consistent and traceable.

Event-Driven Integration and the EDL

• Design event-driven integration patterns that feed the EDL, using messaging and an enterprise service bus to capture changes from operational systems.
• Define standards for event schemas, change data capture, ordering, replay, and error handling.
• Leverage streaming data ingestion
• Coordinate with Solutions Architects so that product teams publish and consume data through approved EDL patterns rather than point-to-point integrations.

Data Products and Sharing

• Define reusable data products with clear ownership, documented schemas, lineage, quality expectations, and access policies.
• Establish the publishing model that makes data products discoverable and consumable by enterprise, agency, and authorized industry consumers.
• Apply data product principles influenced by data mesh, while keeping ownership and decentralization decisions aligned with program direction.
• Define versioning and deprecation practices so that consumers can depend on stable interfaces.

Governance, Quality, and Secure Access

• Define data governance, metadata, and cataloging standards, including business and technical metadata.
• Design Attribute-Based Access Control (ABAC) policies so that authorization reflects user, resource, and mission attributes.
• Establish data quality, lineage, and stewardship practices across the EDL.
• Ensure data handling aligns with privacy, security, and federal compliance requirements, in partnership with cybersecurity teams.

AI-Ready Data Enablement

• Ensure the data architecture supports analytics, machine learning, and Generative AI use cases, including feature reuse and retrieval patterns.
• Partner with Data Scientists and Solutions Architects to make trusted, well-documented data available for AI workloads.
• Define how sensitive data is protected and masked when used for model development and evaluation.

Required Qualifications:

• Minimum of 8 years with BS/BA; Minimum of 6 years with MS/MA; Minimum of 3 years with PhD
• Demonstrated experience in enterprise data architecture across operational and analytical systems.
• Hands-on experience with at least one modern data engineering stack.
• Strong knowledge of relational and non-relational databases (eg SQL, NoSQL, Hadoop, Spark, Kafka, Kinesis) and data modeling.
• Experience designing event-driven and streaming data integration.
• Experience with programming languages i.e Java, SQL, Scala, Python etc
• Experience with schema definition, data governance, metadata management, and data quality practices and software engineering best practices including secure, testable, and maintainable code
• Experience with cloud data platforms, preferably AWS.
• Active Secret Clearance
• Local to the Washington, D.C Metro area 

Preferred Qualifications:

• Experience with data lake and lakehouse architectures.
• Experience with streaming and integration technologies such as Kafka, Amazon Services (Glue, Kinesis, MSK, EventBridge etc.),
• Experience with data catalog and governance tooling.
• Experience implementing ABAC or other attribute-based authorization models.
• Familiarity with data mesh and data product operating models.
• Experience supporting DCSA, DoD, or other federal programs.
• Experience designing and building Data Lakes, Data Warehouses, and scalable data platforms in the cloud