AI Data Engineer (Data & Analytics)

Data and Analytics at COMPLY:

COMPLY is the world's leading aggregator of financial and regulatory data to support compliance. Our mission is to help financial institutions meet their regulatory obligations with confidence, clarity, and speed. We ingest, process, and enrich vast volumes of complex, high-variance data from hundreds of brokers, data providers and other sources of information across the US and beyond. The scale, diversity, and importance of this data creates unique technical challenges and opportunities for innovative engineers.

The COMPLY Data Platform is a strategic initiative at the heart of this mission. We are building a modern, cloud- native semantic layer from the ground up — using JSON-LD as our semantic language — to power AI-driven insights, regulatory analytics, and next-generation data products.

Our AI Data Engineers work hand-in-hand with the data engineers, architects, and ontologist to translate semantic models into production-grade knowledge graphs, embedding pipelines, and RAG architectures that make Comply's data genuinely AI-ready.

The Role:

We are looking for an AI Data Engineer to implement and operationalize Comply's semantic layer — turning the ontological models defined by our ontologist and architects into working knowledge graphs, vector search infrastructure, and LLM-powered pipelines. This is a hands-on engineering role at the intersection of knowledge representation, AI infrastructure, and data platform engineering. You will own the delivery of semantic layer components, collaborate closely with application and data engineering teams, and ensure that AI-ready data products are reliable, performant, and adopted in practice. You will report into the Data and Analytics organization as part of a new team being created to enable future data capabilities in relation to our AI ambitions.

Key Responsibilities:

Semantic Layer Implementation

Implement JSON-LD-based semantic models designed by the ontologist into production data systems
Build and maintain knowledge graph structures that reflect canonical domain models
Develop and manage graph database schemas, queries, and data ingestion pipelines
Ensure semantic consistency between ontology definitions and downstream data products AI & Vector Infrastructure
Design and implement embedding pipelines that represent Comply's financial and regulatory data in vector space
Build and operate vector database infrastructure for semantic search and similarity retrieval
Implement RAG (Retrieval-Augmented Generation) architectures that ground LLM outputs in Comply's proprietary data
Evaluate and integrate LLM tooling and frameworks appropriate to Comply's use cases Data Pipeline & Platform Engineering
Build reliable, observable data pipelines that feed the semantic layer from upstream broker and regulatory data sources
Apply DataOps practices including testing, monitoring, lineage tracking, and SLAs
Work with Data Engineers and Backend Engineers to embed semantic models into APIs and data contracts
Ensure the semantic layer scales with data volume and platform growth Collaboration & Enablement
Partner closely with the Ontologist to ensure implemented models faithfully reflect domain intent
Support consuming application teams in understanding and adopting AI-ready data products
Contribute to resolving cross-domain data integration challenges

Essential Criteria:

Strong hands-on experience in data engineering, with a focus on semantic or AI data infrastructure
Experience building and operating knowledge graphs or graph databases (e.g. Jena Fuseki, Neo4j, Amazon Neptune, or equivalent)
Experience with vector databases and embedding pipelines (e.g. Pinecone, Weaviate, Qdrant, pgvector)
Practical experience implementing RAG architectures or LLM-integrated data pipelines
Familiarity with semantic web standards — JSON-LD, RDF, OWL, or SKOS
Strong Python skills and experience with data pipeline frameworks
Experience with cloud-native data platforms (AWS, Azure, or GCP)

Desirable Criteria:

Exposure to domain-driven design (DDD) and bounded contexts
Experience working directly with ontologists or knowledge engineers
Familiarity with data contracts and data product frameworks
Experience with DataOps tooling, data reliability, or data observability platforms
Background in financial services, RegTech, or compliance data

Way of Working:

Collaborative and pragmatic — focused on adoption and delivery, not theoretical completeness
Comfortable working across engineering and domain boundaries
Able to translate semantic and ontological concepts into concrete engineering decisions
Confident navigating greenfield environments where architecture is still being defined

Impact and Outcomes:

A production-grade semantic layer that is consistent, scalable, and used in practice by consuming teams
Embedding and vector infrastructure that enables Comply's AI-powered data products
Reliable, observable pipelines that maintain semantic quality from ingestion through to consumption
Reduced time-to-value for new AI features through reusable semantic infrastructure