Senior Data Architect, (Senior Consultant, Engineering & Technical Services)

Fearless•Washington, DC

1d•$120,588 - $195,955•Remote

About The Position

At Fearless, we are seeking a Senior Data Architect as part of our Senior Consultant, Engineering & Technical Services career path to design and govern the enterprise semantic data architecture that enables natural language querying, semantic search, knowledge graphs, GraphRAG, and AI-ready analytics across federal ﬁnancial regulatory datasets. This role serves as the technical authority for semantic modeling, ontology development, metadata governance, entity resolution, and cloud-native data architectures supporting cross-domain analysis of structured and unstructured regulatory data. The architect will collaborate with federal stakeholders, data scientists, and engineers to deﬁne semantic layers, graph architectures, and lineage frameworks that integrate complex ﬁnancial and disclosure datasets while maintaining data quality, operational performance, and compliance with federal security and governance standards. At Fearless, this Senior Consultant collaborates with cross functional teams to align the technical vision, implementation, and quality of software systems across development, reliability, testing, and operations. This role will possess the skills to be able to bridge business objectives with technical execution in software development, infrastructure, security, and testing at an advanced application level of proficiency. The Sr. Consultant role will work on projects that allow them to ensure scalable, secure, and reliable solutions while mentoring engineers, fostering collaboration, and aligning stakeholders with product and project goals.

Requirements

Minimum of 10 years of experience designing enterprise data architectures, with at least 5 years focused on cloud-native data platforms.
Experience with knowledge graphs, semantic layers, ontologies, graph databases, and metadata governance.
Experience with AWS, Neptune, OpenSearch, Databricks, and lakehouse architectures.
Experience implementing entity resolution, data lineage, master data management, and data governance solutions.
Familiarity with XBRL taxonomies, financial reporting data, and regulatory datasets.
Strong stakeholder facilitation and data governance experience.
Bachelor's degree in Computer Science, Data Engineering, Information Systems, or related field
Ability to obtain and maintain Public Trust clearance.

Nice To Haves

Experience supporting SEC, Treasury, IRS, CFPB, FDIC, OCC, Federal Reserve, or similar regulatory agencies.
Experience implementing GraphRAG architectures that integrate knowledge graphs with retrieval-augmented generation workflows.
Experience with RDF, LPG, OWL, SKOS, or related semantic technologies and standards.
AWS Certified Solutions Architect Professional.
Databricks Certified Data Architect.

Responsibilities

Lead enterprise semantic architecture strategy and roadmap development to support scalable semantic data capabilities across financial, regulatory, and disclosure datasets.
Design enterprise ontologies supporting SEC financial, regulatory, and disclosure data.
Define semantic layer architectures supporting natural language query (NLQ), semantic search, knowledge graphs, GraphRAG, and agentic AI workflows.
Develop logical and physical architectures for lakehouse environments, knowledge graphs, graph databases, metadata repositories, and vector stores.
Establish metadata governance standards that include machine-readable definitions, business glossaries, data lineage, versioning, and stewardship models.
Define entity resolution strategies and identity management approaches to support consistent semantic integration across enterprise datasets.
Establish architecture patterns leveraging AWS Neptune, OpenSearch, Databricks, lakehouse architectures, and XBRL taxonomies.
Design lineage and traceability capabilities that support auditability and explainable AI.
Ensure compliance with SEC security, governance, and AI requirements.
Collaborate with federal stakeholders to develop semantic models across multiple datasets and forms.
Guide technical teams through architecture reviews and implementation decisions.