Principal Engineer, AI & Data Platform

Reports To: Director, Engineering

Role

This is an enterprise-wide technical leadership role reporting to the Head of AI & Data Platform. Depending on experience, this will be filled at Director or Lead Engineer level. You will own the architecture and delivery of systems that make Bullish’s data not just accessible but intelligible—to business users through conversational analytics, and to AI agents through governed semantic and knowledge layers.

What You’ll Do

• Knowledge Architecture & Semantic Infrastructure. Design and own the enterprise knowledge layer—the governed semantic models, ontologies, and knowledge graph structures that ground both human analytics and AI agents in a single source of truth. Define how business meaning flows from glossaries through data models to agent context.
• Conversational Analytics. Lead the strategy and delivery of natural-language interfaces to business data. Move beyond dashboard-driven BI toward systems where stakeholders query complex datasets conversationally and receive context-rich, citation-backed answers from governed semantic layers.
• Agentic Data Platform. Architect the infrastructure that enables AI agents to discover, reason over, and act on enterprise data. This includes MCP-based tool connectivity, agent-facing data services, and integration with emerging capabilities such as BigQuery Graph, Knowledge Catalog, and the Google Cloud Data Agent Kit.
• Advanced Data Infrastructure. Drive adoption of graph databases, knowledge bases, and hybrid query engines that support multi-hop reasoning, entity resolution, and relationship-aware analytics. Evaluate and integrate technologies at the intersection of structured data, knowledge graphs, and generative AI—including GraphRAG patterns and vector-augmented retrieval.
• Enterprise Data Strategy. Partner with domain stakeholders across trading, treasury, compliance, market intelligence, and media to ensure the data platform serves the full breadth of the business. Own cross-domain data modeling standards and govern the semantic layer that underpins all analytical and AI workloads.
• Evaluation & Trust. Establish evaluation frameworks for AI systems that consume platform data—ensuring groundedness, factual consistency, and output reliability. Build the measurement infrastructure that lets the organization trust what agents produce.
• Technical Leadership. Set architectural direction, mentor engineers, drive build-vs-buy decisions, and represent the team’s technical vision to senior stakeholders. At Director level, operate as a peer to engineering directors across the organization; at Lead level, drive technical excellence and influence architectural decisions across the platform.

What You’ll Bring

• Data & AI Platform Experience. 7+ years in data engineering, analytics, or AI platform roles. Director-level candidates will have 3+ years in a technical leadership position (Director, Principal, Staff, or equivalent); Lead Engineer candidates will have demonstrated technical ownership and mentorship in senior IC roles. Demonstrated experience building and operating enterprise-scale data platforms in production.
• Conversational Analytics & Semantic Layers. Direct experience building natural-language query systems over structured data. Deep understanding of why semantic layers, governed definitions, and business context are prerequisites for accurate conversational analytics—not afterthoughts.
• Knowledge Graphs & Advanced Data Models. Hands‑on experience with graph databases, knowledge graphs, or ontology‑driven data architectures. Understanding of how graph structures enable multi-hop reasoning, entity resolution, and context grounding for AI agents.
• Experience with at least 3 of the following:
  • Graph databases and query languages (Neo4j, TigerGraph, Amazon Neptune, or Big-Query Graph)
  • Knowledge graph construction and ontology modeling (RDF/OWL, property graphs, taxonomy design)
  • GraphRAG architectures (graph-augmented retrieval for grounded generation)
  • Semantic layer and business intelligence platforms (Looker, dbt Semantic Layer, AtScale)
  • Vector databases and hybrid retrieval (Qdrant, Pinecone, pgvector, AlloyDB vector search)
  • Cloud data platforms at scale (BigQuery, Snowflake, Databricks, Spanner)
  • Data cataloging and governance (Google Knowledge Catalog/Dataplex, Collibra, Alation, Atlan)
  • MCP (Model Context Protocol) for agent‑data connectivity
• Agent & AI Systems Expertise. Experience designing systems where AI agents interact with data infrastructure—including tool‑use patterns, structured output generation, and agent orchestration frameworks. Understanding of evaluation methodology for AI systems (groundedness, factual consistency, hallucination measurement).
• Cloud Infrastructure. Strong GCP experience preferred (BigQuery, Cloud Composer, Vertex AI, Dataplex/Knowledge Catalog). Comfort operating in regulated, multi‑region cloud environments with strict data governance requirements.
• Engineering Rigor. Track record of building observable, testable, well‑documented systems. Experience with CI/CD for data and ML pipelines, data quality frameworks, and infrastructure‑as‑code practices.
• Communication & Influence. Ability to translate between deep technical architecture and business strategy. Comfortable presenting to C‑suite stakeholders, aligning cross‑functional teams, and making the case for long‑term platform investments. You write clearly and think in systems.

Nice to Haves

• Experience in financial services, fintech, cryptocurrency, or institutional trading
• Background in data mesh, domain‑oriented data ownership, or federated governance models
• Experience with Google Cloud’s Agentic Data Cloud capabilities (Knowledge Catalog, Big-Query Graph, Data Agent Kit, MCP Toolbox for Databases)
• Familiarity with dbt for transformation and data modeling at scale
• Experience building or operating streaming data infrastructure alongside batch processing
• Background in compliance‑sensitive environments (SOX, regulatory reporting, audit systems)
• Published work, conference talks, or open‑source contributions in knowledge engineering, semantic AI, or conversational analytics

EQUAL OPPORTUNITY

In an effort to attract, retain, develop and promote the most qualified individuals, CoinDesk is committed to treating all applicants and employees in a nondiscriminatory manner with respect to the terms and conditions of employment, without regard to race, color, religion or belief, sex, national or ethnic origin, ancestry, age, marital status, sexual orientation, gender identity, veteran status/service, physical or mental disability, or any other classification protected by applicable law. This mandate governs all aspects of employment, including recruitment, selection, promotion, training, education, social and recreation programs, compensation, discipline, termination and access to benefits.

ACCOMMODATION

CoinDesk is also committed to providing reasonable accommodations to individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the application process, please send an e‑mail to recruiting@coindesk.com and let us know the nature of your request.

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