Big 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).