Babel Street - Head of Data, Platform and Intelligence
Requirements
• 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. • Benefits at Babel Street (just to name a few...) • Health Benefits: Babel Street covers 85-100% monthly premium costs for Medical, Dental, Vision, Life & Disability insurances – for you and your family! • Retirement Plans: Babel Street offers both a Traditional and Roth 401(K) with a very competitive match. • Unlimited Flexible Leave: We trust our employees to manage their own time and balance their personal and work lives. • Holidays: Babel Street provides employees with 12 paid Federal Holidays • Tuition Reimbursement: We are committed to investing in our employees. One way we do that is with our Tuition Reimbursement Program for continuing education.
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 practices
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