Principal Architect

eSimplicity•Columbia, MD

7d•$147,700 - $168,400•Remote

About The Position

The Principal Architect will lead reference architecture, cross-team technical standards, architecture governance, decision-making, interoperability patterns, and major technical trade-off analysis for a large-scale federal data and analytics modernization program. The program includes a governed cloud data platform, analytics products, APIs, AI-enabled capabilities, public-facing reporting, and integrated user services. This role will guide technical direction across platform foundations, data supply chain, metadata maturity, analytics, public-facing products, AI/ML platform enablement, APIs, partner integrations, security, portability, and transition readiness. The Principal Architect will work with customer stakeholders, program leadership, engineering, data, product, security, AI, analytics, and partner teams to make architectural decisions transparent, reusable, secure, and aligned to customer objectives.

Requirements

12+ years experience in software engineering, including implementing engineering best practices, iterative/continuous engineering principles
Bachelor’s degree in Computer Science, Information Systems, Engineering, Math, or other related scientific or technical discipline. With twelve years of general information technology
All candidates must pass public trust clearance through the U.S. Federal Government. This requires candidates to either be U.S. citizens or pass clearance through the Foreign National Government System which will require that candidates have lived within the United States for at least 3 out of the previous 5 years, have a valid and non-expired passport from their country of birth and appropriate VISA/work permit documentation
Demonstrated ability to lead architecture for complex cloud, data, analytics, API, AI/ML, or digital platform ecosystems with multiple teams and stakeholders.
Experience establishing reference architectures, technical standards, architecture governance, decision records, interoperability patterns, and technical trade-off recommendations.
Working knowledge of cloud architecture, lakehouse or comparable data platforms, governance layers, infrastructure as code, CI/CD, observability, identity and access, API patterns, data sharing, dashboard platforms, and AI/ML enablement patterns.
Ability to apply security, privacy, accessibility, data-use, Government data-rights, portability, reuse, and transition-readiness constraints to architecture decisions.
Experience coordinating architecture across product, engineering, data, AI, BI, security, support, and external integration teams in a transparent agile delivery environment.
Knowledge of federal system delivery expectations such as FISMA, NIST-based controls, FedRAMP-authorized cloud services, ATO support, access reviews, and continuous monitoring.
Ability to comply with customer-specific security, privacy, accessibility, quality, training, and data-handling requirements for assigned systems and data.

Nice To Haves

Experience supporting federal, public sector, healthcare, or other regulated data, analytics, oversight, reporting, or public transparency programs.
Experience with modern cloud platforms, infrastructure-as-code tooling, lakehouse or comparable data platforms, data governance layers, distributed processing, source control, CI/CD, API services, serverless patterns, secure data-sharing patterns, BI platforms, workflow tools, identity services, observability platforms, AI/ML platform capabilities, model serving, vector search, automated model evaluation, and retrieval-augmented generation patterns.
Experience with tenant onboarding, federated data participation, governed data exchange, public-facing data services, API factory patterns, metadata maturity, and multi-tier data trust architectures.
Experience with responsible AI governance, AI capability-gate roadmaps, model serving readiness, AI observability, inference logging, and human oversight for AI-enabled services.
Hands-on experience building and optimizing data pipelines within the Databricks platform
Preferred certifications may include cloud solutions architecture, data platform architecture, infrastructure as code, AWS, Databricks TOGAF, SAFe, cloud security, CISSP, FinOps, data architecture, or AI/ML platform credentials.

Responsibilities

Own reference architecture, cross-team technical standards, architecture governance and decision-making, interoperability patterns, and major technical trade-off analysis.
Lead applicable technical coverage across cloud and DevSecOps engineering, data engineering, data governance and metadata specialization, BI and analytics development, AI/ML platform engineering, security engineering and compliance, API services, partner integrations, and domain architecture.
Guide architecture for platform-first delivery, approved technology direction, infrastructure as code, governed data trust patterns, API-first reuse, secure data sharing, AI-enabled service patterns, public-facing dashboards, and transition-ready artifact management.
Coordinate with customer stakeholders, cloud and platform partners, security and privacy stakeholders, data owners, product teams, and downstream consumers on architecture reviews, environment changes, source onboarding, integrations, public-facing releases, and major technical decisions.
Develop and maintain architectural artifacts in customer-accessible systems, including reference architectures, decision records, diagrams, standards, runbooks, interface documentation, joint operating agreements, service guides, data-flow documentation, and transition materials.
Evaluate new or materially changed services against security, privacy, accessibility, cost transparency, observability, operational readiness, metadata maturity, AI governance, portability, and reuse criteria.
Support joint operating agreements and cross-platform coordination for source-system and downstream-system interfaces, ingestion lead times, schema and metadata changes, incident boundaries, release dependencies, security and privacy responsibilities, support handoffs, and transition responsibilities.
Promote out-of-the-box, native, reusable, open-source-aware, and portable solutions while minimizing unnecessary proprietary dependencies and avoiding contractor-managed external data systems.