Data Architect

Role Overview

• Design and implement scalable enterprise data architectures leveraging AWS and Apache ecosystem technologies (e.g., Spark, Iceberg).
• Architect modern AI-enabled data platforms, including support for machine learning, LLM integration, and retrieval-augmented generation (RAG) patterns.
• Develop and maintain conceptual, logical, and physical data models, including Entity Relationship Diagrams (ERDs).
• Architect modern data lakehouse and data warehouse solutions using Apache Iceberg and cloud-native services.
• Define and enforce standards for data integration, data quality, and data lifecycle management.
• Design and implement Knowledge Graph architectures, integrating structured and unstructured data sources.
• Design and implement Knowledge Graphs and semantic data layers using ontologies, taxonomies, and linked data principles.
• Apply GraphRAG architectures to enhance LLM-based applications with context-aware, explainable data retrieval.
• Develop and manage ontologies and semantic models to enable interoperability, data discovery, and advanced analytics.
• Integrate AI/ML and generative AI capabilities into enterprise data ecosystems, including vector databases and embedding pipelines.
• Ensure alignment between data architecture and AI governance, including model transparency, traceability, and responsible AI practices.
• Establish and manage enterprise metadata frameworks, including data dictionaries, business glossaries, and technical metadata repositories.
• Lead or support stakeholder listening campaigns to gather input from executives, data leaders, and practitioners across the enterprise.
• Collaborate with stakeholders to identify data challenges, AI use cases, and opportunities for advanced analytics and automation.
• Support analysis of alternatives (AoA) for data and AI tools/platforms, providing recommendations based on cost, capability, and mission fit.
• Track and report on data strategy progress, maturity improvements, and program outcomes.

Requirements

• Bachelor’s degree in Computer Science, Information Systems, Data Science, or related field or comparable experience.
• 8+ years of experience in data architecture, data engineering, or enterprise data management.
• Demonstrated experience integrating AI/ML or generative AI capabilities into data platforms.
• Hands-on experience with: AWS data services (e.g., S3, Glue, Redshift, Lake Formation) Apache technologies (e.g., Spark, Iceberg, Hive) Relational databases.
• Strong expertise in data modeling and ERD development.
• Experience designing or implementing Knowledge Graphs, ontologies, or semantic data models.
• Familiarity with Graph-based retrieval approaches (e.g., GraphRAG or similar patterns).
• Experience implementing metadata management, data cataloging, and data governance solutions.
• Demonstrated experience supporting federal data strategy initiatives or OCDO organizations.
• Strong understanding of data quality, lineage, observability, and AI data readiness frameworks.
• Proficiency with AI-assisted tools and workflows (e.g., LLM copilots, automated code generation, data augmentation tools).
• Ability to communicate complex technical concepts to non-technical stakeholders.
• U.S. Citizenship required; ability to obtain and maintain a federal clearance.

Tech Stack

• Amazon Redshift
• Apache
• AWS
• Cloud
• Spark

Benefits

• Flexible work arrangements
• Professional development opportunities