Data Architect

Anika Systems is seeking a highly skilled Data Architect to lead the design and implementation of enterprise data architectures supporting federal clients. This role will be instrumental in shaping data strategy, enabling data-driven decision-making, and supporting the establishment and maturation of Office of the Chief Data Officer (OCDO) organizations. The ideal candidate brings deep expertise in enterprise data modeling, cloud-based data platforms, metadata management, and data governance, along with hands‑on experience applying AI/ML, Knowledge Graphs, and semantic technologies to modern data ecosystems. This role requires a forward-thinking architect who embraces AI-driven development workflows and can integrate emerging techniques such as GraphRAG into enterprise data platforms. This opportunity is 100% remote. Key Responsibilities Enterprise Data Architecture & Engineering 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. AI, Knowledge Graphs & Semantic Architecture 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. Leverage AI-assisted development tools (e.g., code generation, data pipeline automation, metadata enrichment) to improve delivery speed and quality. Ensure alignment between data architecture and AI governance, including model transparency, traceability, and responsible AI practices. Metadata, Data Catalog, and Data Management Establish and manage enterprise metadata frameworks, including data dictionaries, business glossaries, and technical metadata repositories. Support implementation or optimization of Enterprise Data Resource Management Systems (EDRMS) and data catalog tools (e.g., Collibra, ServiceNow, or similar platforms). Ensure referential integrity and traceability between data assets, metadata ontologies, and enterprise data initiatives. Design systems that enable data lineage, observability, and quality monitoring, including AI-generated metadata and lineage tracking. Stakeholder Engagement & Data Governance 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 the development and maintenance of data governance frameworks, policies, and standards, including AI and semantic governance. Maintain and prioritize a data initiatives backlog, ensuring alignment with mission needs and stakeholder priorities. Agile Delivery & Continuous Improvement Work within Agile frameworks to iteratively deliver data architecture and AI‑enabled solutions. 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. Continuously refine data architecture based on stakeholder feedback, emerging AI capabilities, and evolving organizational needs. Required Qualifications 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. Preferred Qualifications Experience supporting agencies such as SEC, DHS, Treasury, or Federal Reserve System. Familiarity with Evidence Act, Federal Data Strategy, and CDO Council guidance. Experience with Collibra, Informatica, Alation, or similar data catalog tools. Experience with graph databases (e.g., Neo4j, Amazon Neptune) and vector databases. Knowledge of data maturity frameworks (e.g., EDM DCAM, TDWI). AWS certifications or data architecture certifications. Experience implementing RAG or GraphRAG solutions in production environments. Familiarity with semantic web standards (RDF, OWL, SPARQL). #J-18808-Ljbffr