Senior Data Architect

CACI•Oklahoma City, OK

17h•Onsite

About The Position

We are seeking an experienced Senior Data Architect to lead the design, modernization, and governance of our enterprise data ecosystem. This role is critical in preparing our data platforms, pipelines, and governance frameworks for large-scale AI and advanced analytics initiatives. The ideal candidate brings deep technical expertise, strong architectural instincts, and a strategic view of how data must evolve to support AI-driven business outcomes. The right candidate will work with the Data Reporting Team and their customers to facilitate communication and determine software objectives that align with stated and understood business needs. In this role, you will identify, define, and document business processes and software/integration requirements, test cases, etc.

Requirements

5+ years of experience in data architecture, data engineering, or enterprise architecture roles.
Proven experience designing large-scale data ecosystems (cloud, distributed systems, lakehouse architectures).
Strong understanding of AI/ML data needs including: data labeling and curation feature engineering and feature stores vector embeddings and retrieval-augmented architectures GPU/ML workload data provisioning
Expertise with modern data technologies (e.g., Databricks, Snowflake, Synapse, BigQuery, AWS/GCP/Azure data services).
Advanced SQL and data modeling skills (logical, physical, dimensional, semantic modeling).
Deep knowledge of data governance tools, metadata management, and lineage frameworks.
Strong communication skills and the ability to translate complex data concepts into business value.

Nice To Haves

Bachelor’s Degree in Information Technology, Computer Science, Computer Information Systems, Business, or applicable field.
ITIL v3 or ITIL v4 Certification.
Working knowledge of service delivery frameworks and methodologies including – Agile, PMBOK, ITIL, etc.
Experience with Cognos, PowerBI and SQL

Responsibilities

Lead the architecture, design, and implementation of enterprise data platforms that support analytics, machine learning, and AI workloads.
Define and drive an enterprise-wide AI data readiness strategy , including data quality, metadata management, lineage, and feature availability.
Develop and maintain scalable data models, data integration patterns, and data pipelines optimized for AI and real‑time insights.
Partner with Data Science, Engineering, Security, and Product teams to ensure data accessibility, governance, and reliability.
Establish and enforce data architecture standards, best practices, and design patterns that enable interoperability across systems.
Assess current-state data environments and develop target-state architectures to support AI/ML model training, deployment, and monitoring.
Provide technical leadership on data modernization, including cloud migration, data lakehouse implementation, and event-driven architectures.
Evaluate emerging technologies, especially around MLOps, vector databases, semantic layers, and LLM integration—to ensure platforms support future AI capabilities.
Champion the adoption of robust data governance practices, ensuring compliance, privacy, and security across data assets.
Mentor engineering teams and guide solution architects on data decisions aligned with enterprise strategy.