Principal Data Platform Engineer

Responsibilities

Peraton is hiring a Data Architecture, Senior Advisor . This work is 100% remote.

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.

- 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.

Qualifications
Required Qualifications

- 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

Preferred Qualifications

- 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 expertis