Data Architecture, Senior Advisor

Peraton•,

1d•$146,000 - $234,000•Remote

About The Position

Peraton is hiring a Data Architecture, Senior Advisor. This role is the founding owner of a greenfield data and AI platform supporting federal background investigation work. You will design and build the data platform from scratch — owning the architecture end to end, partnering with cloud engineering across the infrastructure boundary, and laying the foundation a growing data science and machine learning team will rely on to deliver state-of-the-art ML and AI capabilities to customers. This is a hands-on role in a high-trust environment where FedRAMP Moderate, NIST 800-171, and CUI handling are first-class design constraints, not afterthoughts. It's also high-ownership work: a modern platform built deliberately on real mission problems. Active clearance preferred; candidates able to obtain one encouraged to apply.

Requirements

U.S. citizenship required.
Must be able to obtain and maintain a T5/SSBI federally adjudicated clearance; active clearance preferred.
8+ years in data engineering / data platform engineering, with demonstrated principal-level ownership.
Has stood up a data platform or lakehouse from scratch — owning the architecture and build end to end, not operating an inherited one.
Design of batch (and, where needed, streaming) data pipelines and SQL-based transformations on a lakehouse/Delta foundation, with sound analytical data modeling.
Infrastructure-as-code (Terraform) and CI/CD for data workloads, including environment promotion from development to production.
Platform-level data governance: cataloging, lineage, and fine-grained access control.
Hands-on cloud experience with a major provider (Azure preferred; AWS or GCP considered).
Strong proficiency in Python and SQL.
Track record partnering across an infrastructure/security boundary and setting technical standards for other engineers.
Excellent analytical, troubleshooting, and communication skills.
Minimum 12 years work experience with BS/BA

Nice To Haves

Hands-on Databricks: Unity Catalog, Databricks Asset Bundles, MLflow.
Experience in regulated or accredited environments: FedRAMP, NIST 800-171, CMMC, CUI handling, or the ATO/RMF process.
Active security clearance (T5/SSBI or higher).
Government or defense contracting experience.
Familiarity with MLOps patterns (model registry, model serving) to support a data science team.
Cost governance / FinOps discipline for cloud data platforms.
Spark / PySpark — relevant since the platform is Databricks, though the data volume here does not demand distributed-scale expertise.
Ability to translate business needs into performant, well-architected data solutions.
Strong collaboration across technical and non-technical teams.
Clear documentation and communication in fast-paced environments.

Responsibilities

Design and stand up the organization's data and AI platform from the ground up — architecture, compute, storage, and the lakehouse foundation.
Codify the platform as infrastructure-as-code (Terraform) and build the CI/CD pipelines that promote work from development through to the accredited production environment.
Establish data governance, cataloging, lineage, and fine-grained access control as foundational, not bolted on later.
Build and own the ingestion, transformation, and pipeline layer that turns raw and synthetic data into governed, analysis-ready data products.
Design the platform to operate within FedRAMP Moderate, NIST 800-171, and CUI constraints, treating compliance as a first-class architectural requirement.
Define the artifact promotion process so only signed, validated artifacts cross into the accredited environment.
Partner with cloud engineering across the infrastructure/security boundary, with clear ownership of the in-platform layer.
Enable the data science and ML team with the platform capabilities, governed data, and tooling they need to ship models and AI features into the product.
Own platform reliability, performance, and cost discipline as usage scales.
Set the engineering standards, patterns, and documentation a growing data team will build on.