Head of Data, Platform and Intelligence

ROLE SUMMARY 

As Head of Data you will define and lead Babel Street’s North Star Data Strategy and Architecture, building a cohesive, scalable, and AI-native data platform that transforms fragmented systems into a unified foundation for intelligence, analytics, and product innovation. 

You will own the full lifecycle of data across the organization, from ingestion and storage to semantics, retrieval, and productization. Your mandate is to unify fragmented systems into a cohesive, scalable, and AI-ready data platform that directly enables investigative, analytical, and operational workflows across Babel Street’s product suite. 

You will work closely with Product and Engineering leadership, to ensure that data is not just infrastructure, but a core competitive advantage. This includes powering Babel Street’s Knowledge Graph, enabling agentic and generative AI systems, and delivering data capabilities that are reliable, performant, and economically efficient at scale. 

This role requires deep technical expertise, strong architectural judgment, and the ability to translate complex data challenges into customer-impacting intelligence capabilities. 

This hybrid role will be based in our Reston, VA or Somerville, MA office. 

ROLE SPAN 

This role spans four integrated domains: 

1. Data Platform & Storage Architecture 

You will help define and evolve Babel Street’s unified data platform, consolidating warehouse, search and object storage systems into a cohesive scalable foundation. This includes establishing clear patterns for when and how to use analytical warehouses, search/index systems, and object storage to support diverse workloads across the business. 

You will architect systems that operate at petabyte scale, ensuring high performance, reliability, and flexibility across batch and real-time data. A key focus will be driving platform rationalization while maintaining continuity of operations and minimizing risk. 

You will also establish standards for data ingestion, transformation, and lifecycle management, ensuring consistency and efficiency across the platform.  

2. Data Semantics, Knowledge Graph & Identity 

You will own the semantic foundation of Babel Street’s data ecosystem, defining how data is modeled, connected, and understood across products and systems.  

This includes building and evolving the company's knowledge graph, including entity resolution, identity modeling, and relationship mapping across disparate data sources. You will establish ontology and schema strategies that ensure consistent interpretation of data across teams, products, and AI systems. 

Your work will enable graph-integrated reasoning and provide the structures context required for intelligence workflows and AI-driven applications. 

3. Data Access, Retrieval & AI-Enablement 

You will design and operate data access patterns that power both human and machine consumption of data, including API’s, query layers and retrieval systems.  

This includes enabling hybrid retrieval approaches across structured, unstructured and vector-based data to support LLMs, RAG pipelines, and agentic systems. You will ensure that data is accessible in a way that is performant, scalable, and optimized for AI workloads. 

You will partner closely with AI and Applied ML teams to ensure seamless integration between data systems and model-driven capabilities, enabling reliable, explainable, and efficient intelligence generation. 

4. Data Productization, Governance & Economics 

You will establish a data-as-a-product operating model, ensuring that data assets are discoverable, reusable, and governed with clear ownership and accountability.  

This includes defining contracts, enforcing quality standards, and implementing metadata and governance frameworks that scale across the organization. 

You will also own the economics of the data platform, ensure efficient use of storage and compute, and optimize cost per query, cost per workload, and overall system efficiency. A key focus will be enabling scalable AI usage through efficient data retrieval and storage strategies. 

 

KEY RESPONSIBILITIES 

Define the North Star Data Architecture 

• Establish and evolve the target-state data architecture, aligning storage, compute, search, and access patterns into a unified platform
• Drive architectural clarity across warehouse, search, object storage, and real-time systems
• Ensure consistency in schemas, metadata, and governance frameworks across all data domains 

Build an AI-Native Data Foundation 

• Design a data platform optimized for AI and agentic workloads, including: 
  API-first, agent-callable data services
• Hybrid retrieval patterns (search + analytical + vector)
• Real-time and batch data unification
• Enable scalable support for LLMs, RAG pipelines, and intelligence workflows 

Own Data as a Product 

• Establish a data-as-a-product operating model, enabling discoverable, reusable, and well-governed data assets
• Define and standardize data contracts, ownership models, and domain boundaries
• Translate platform capabilities into customer-facing data products and differentiators 

Lead Platform Rationalization and Evolution 

• Rationalize and evolve the current ecosystem (e.g., BigQuery, Elasticsearch/OpenSearch, S3) into a cohesive and cost-efficient architecture
• Lead phased, low-risk migrations and consolidations aligned to business priorities
• Balance short-term pragmatism with long-term architectural integrity 

Own Performance, Reliability, and Cost Economics 

• Accountable for performance, scalability, and reliability of all data systems
• Establish clear unit economics for data (e.g., cost per query, cost per workload, storage efficiency)
• Implement strong observability, SLOs, and incident management practicesgorustawsgcpaidataanalyticsproductdesign