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

Fearless•Washington, DC

1d•$120,588 - $195,955•Hybrid

About The Position

At Fearless, we are seeking a Senior Data Engineer as part of our Senior Consultant, Engineering and Technical Services career path to design and build cloud-native data engineering pipelines supporting semantic processing, knowledge graph construction, and AI-enabled analytics for federal financial regulatory systems. This role supports a large-scale data modernization initiative that processes structured and unstructured regulatory filings, financial disclosures, and enterprise datasets to enable semantic search, natural language query, and GraphRAG capabilities across federal data assets. 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 7 years of data engineering experience
Experience building cloud-native data pipelines.
Proven experience building data pipelines using Python, SQL, Spark, Databricks, and AWS services.
Demonstrated experience implementing data lakes, lakehouse architectures, and ETL/ELT frameworks with metadata management, data lineage, and data quality controls.
Experience with graph databases such as Neptune, OpenSearch, and vector databases supporting semantic search and AI/ML workflows.
Familiarity with XBRL-formatted data, financial reporting datasets, or regulatory filings in federal or financial services contexts.
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 implementing GraphRAG solutions and natural language query architectures using Databricks Genie and NLQ architectures.
Experience building knowledge graphs exceeding hundreds of millions of nodes and relationships using AWS Neptune and OpenSearch.
Experience supporting federal AI modernization initiatives.
AWS Data Analytics Specialty certification or Databricks Data Engineer Professional certification.

Responsibilities

Design and develop enterprise data pipelines supporting structured data ingestion, unstructured document ingestion, metadata extraction, semantic enrichment, entity resolution, and knowledge graph population.
Build and maintain AWS-native ETL/ELT solutions.
Develop data pipelines supporting XBRL processing, financial disclosures, regulatory filings, and enterprise datasets.
Implement semantic processing architectures using Databricks, Spark, Delta Lake, OpenSearch, and Neptune.
Develop graph generation pipelines that produce nodes, edges, relationships, and ontological mappings.
Build metadata extraction and lineage tracking capabilities.
Support GraphRAG and semantic search implementations.
Implement APIs supporting natural language query (NLQ) and semantic retrieval services.
Develop CI/CD pipelines and Infrastructure-as-Code deployments.
Build automated testing frameworks and data quality monitoring capabilities.
Implement security controls that support federal compliance requirements.
Optimize pipeline performance, scalability, and cloud costs.
Support Agile delivery and sprint-based development.