Cloud Data Architect

About the role

The team delivers scalable, secure data platforms for federal clients. This role defines and owns enterprise architecture for a large Veteran Affairs analytics environment. Success depends on enabling data science, governance, and compliance for mission-critical systems.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Ownership of the multi-cloud data architecture is established for Azure and Azure Databricks, and documentation captures decisions for enterprise stakeholders. Governance, security, and compliance strategies are designed to protect sensitive Veteran PII and PHI while meeting federal requirements. Enablement of data science and AI/ML capabilities occurs through model deployment infrastructure and scalable data pipelines that serve analytics consumers.

Requirements

Authorization to work in the United States is required, and some federal contract roles may require U.S. citizenship and the ability to obtain a federal background investigation or security clearance. A Bachelor's Degree is required as the baseline educational qualification for this position. Proven experience designing and managing enterprise cloud data architectures in Azure demonstrates readiness for owning complex technical decisions. Hands-on experience with Azure Databricks for data engineering, analytics, and AI/ML workloads shows capability in the primary platform for this role. Familiarity with Power BI or similar visualization platforms supports effective data storytelling and stakeholder communication. Familiarity with AWS data services is valuable given the multi-cloud environment and integration needs. A strong background in data governance, security compliance, and federal data standards including FISMA and NIST 800-53 ensures appropriate risk management. Experience with large-scale data migration and architecture modernization efforts prepares the team for transformational initiatives. The ability to produce clear, detailed technical architecture documentation enables alignment across technical and executive audiences. Thriving in a remote, collaborative Agile environment and enjoying work across engineering, product, and government stakeholder teams supports effective delivery. Clear communication of architecture decisions to leadership and integration design with engineers ensures shared understanding and alignment.

Practical notes

Remote work is fully supported, and U.S. authorization requirements apply. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data architecture roles focus on structuring information systems to support analytics and operations. Azure and Azure Databricks are central tools for data engineering and platform workloads in this environment. Data governance frameworks define policies for quality, privacy, and access control. Agile methods coordinate iterative delivery across distributed teams. Technical documentation serves as the primary mechanism for sharing decisions and rationale.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.