Head of Data, Platform and Intelligence

This role spansfourintegrated domains:

1. Data Platform & Storage Architecture

You will help define andevolveBabel Streets unified data platformconsolidatingwarehousesearchand object storage systems into a cohesive scalable foundation. This includesestablishingclearpatterns for when and how to use analytical warehousessearch/index systems and object storage to support diverse workloads across the business.

You will architect systems thatoperateat petabyte scale ensuring high performance reliability and flexibility across batch and real-time data. A key focus will be driving platform rationalization whilemaintainingcontinuity of operations and minimizing risk.

You will alsoestablishstandards 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 Streets data ecosystem defining how data is modeled connected and understood across products and systems.

This includes building and evolving thecompanysknowledge graph including entity resolution identitymodelingand relationship mapping across disparatedata sources. You willestablishontology 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 contextrequiredfor intelligence workflows and AI-driven applications.

3. Data Access Retrieval & AI-Enablement

You will design andoperatedata access patterns that power both human and machine consumption of data including APIs querylayersand retrieval systems.

This includes enabling hybrid retrieval approaches across structuredunstructuredand vector-based data to support LLMs RAGpipelines andagentic systems. You willensure that data isaccessiblein a way that isperformantscalableand optimized for AIworkloads.

You will partnercloselywith AI and Applied ML teams to ensure seamless integration between data systems and model-driven capabilitiesenablingreliable explainable and efficient intelligence generation.

4. Data Productization Governance & Economics

You willestablisha data-as-a-productoperatingmodel ensuring that dataassetsare discoverablereusableand governed with clear ownership and accountability.

This includes defining contracts enforcingqualitystandards and implementing metadata and governance frameworks that scale across the organization.

You will also own the economics of the data platformensureefficient use of storage and compute andoptimizecost per query cost per workload and overall systemefficiency. A key focus will be enabling scalable AI usage through efficient data retrieval and storage strategies.

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 platformoptimizedfor 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-productoperatingmodel 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 ( 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

• 10 years of experience indata platforms dataengineeringor 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:

• Bachelors degree in Computer Science Engineering ora relatedtechnical fieldrequired.
  Mastersdegree or PhD preferred.