Head of Data, Platform and Intelligence

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.

What you will do:

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 practices

What you will bring:

• 10+ years of experience in data platforms, data engineering, or distributed systems

• Strong background working within multi-cloud or hybrid environments, including hands-on experience with Google Cloud Platform (GCP)

• Proven experience designing and evolving large-scale data platforms through major architectural transitions (e.g., warehouse, search, Lakehouse, or multi-cloud transformations)

• Deep expertise across multiple data paradigms, including:

• Analytical warehouses

• Search/index systems

• Object storage and distributed data systems

• Experience building platforms that support AI, ML, or agent-driven systems

• Familiarity with vector search, retrieval architectures, and modern AI data patterns

• Experience with graph-based data models, entity resolution, or knowledge graphs

• Strong communication skills and the ability to collaborate effectively across technical and non-technical teams.

• Experience operating in regulated, high-stakes, or mission-critical environments is strongly preferred.

Education:

• Bachelor’s degree in Computer Science, Engineering, or a related technical field required. 
Master’s degree or PhD preferred.