Data Architect

Remote
Full Time
Experienced
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).
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